Artificial Intelligence for Business Creativity 9781032262987, 9781032263021, 9781003287582

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Artificial Intelligence for Business Creativity

Artificial Intelligence for Business Creativity provides an in-depth examination of the integration of Artificial Intelligence (AI) into the business sector to foster creativity. The book explores the interplay between micro-level individual creativity and macro-level organizational innovation through the lens of AI. It delves into three crucial areas where AI can stimulate business creativity: product and service design, optimized processes, and enhanced organizational collaboration. The authors also highlight the versatility and capability of generative AI systems in promoting creativity and innovation. Intended for business leaders, managers, entrepreneurs, and those interested in AI and creativity, the book offers practical guidance and insightful recommendations on how organizations can effectively utilize AI to enhance their creative process. By offering a comprehensive understanding of the role of AI in fostering creativity, the book equips its readers with the tools to stay ahead in the rapidly changing landscape of AI and creativity. This book is a valuable resource for anyone seeking to understand the impact of AI on business creativity and how to effectively leverage it to foster creativity and innovation in their organization. It is a must-read for anyone looking to increase their knowledge and understanding of AI and its impact on business creativity. Margherita Pagani, Ph.D., HDR, is a full Professor of Digital and Artificial Intelligence in Marketing, Associate Dean of SKEMA AI School for Business, and Director of SKEMA Research Centre for Artificial Intelligence at SKEMA Business School. She also serves as an Advisor for the European Economic and Social Committee (EESC) and is Associate Editor for Micro & Macro Marketing. Her research interest focuses on the impact of AI on consumer behavior and on digital ecosystems. She has previously held positions at emlyon business school, Bocconi University and MIT's Sloan School of Management and has received global recognition for her research and publications through various awards. Renaud Champion is Founder of PRIMNEXT, an early-stage investment company specialized in valuing disruptive technologies, scaling-up companies operating in advanced robotics and artificial intelligence. He is an advisor to the European Commission on its strategy for AI, Data and Robotics. He created the Artificial Intelligence in Management Institute and as an investor he founded Robolution Capital, the first VC fund dedicated to AI and Robotics.

Routledge Focus on Business and Management

The fields of business and management have grown exponentially as areas of research and education. This growth presents challenges for readers trying to keep up with the latest important insights. Routledge Focus on Business and Management presents small books on big topics and how they intersect with the world of business. Individually, each title in the series provides coverage of a key academic topic, whilst collectively, the series forms a comprehensive collection across the business disciplines. Pop-Up Retail The Evolution, Application and Future of Ephemeral Stores Ghalia Boustani Building Virtual Teams Trust, Culture, and Remote Work Catalina Dumitru Fostering Wisdom at Work Jeff M. Allen Artificial Intelligence, Business and Civilization Our Fate Made in Machines Andreas Kaplan Power in Business Relationships Dynamics, Strategies and Internationalisation Dariusz Siemieniako, Maciej Mitręga, Hannu Makkonen and Gregor Pfajfar For more information about this series, please visit: www.routledge.com/RoutledgeFocus-on-Business-and-Management/book-series/FBM

Artificial Intelligence for Business Creativity Edited by Margherita Pagani and Renaud Champion

First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 selection and editorial matter, Margherita Pagani and Renaud Champion; individual chapters, the contributors The right of Margherita Pagani and Renaud Champion to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-032-26298-7 (hbk) ISBN: 978-1-032-26302-1 (pbk) ISBN: 978-1-003-28758-2 (ebk) DOI: 10.4324/9781003287582 Typeset in Times New Roman by Apex CoVantage, LLC

Per aspera ad astra “Through hardship to the stars” Seneca

Contents

About the authors Foreword: the intersection of AI and creativity

ix xiii

IGOR JABLOKOV

Introduction

1

MARGHERITA PAGANI AND RENAUD CHAMPION

PART ONE

Artificial Intelligence and creativity

7

1

9

Creativity and innovation in the age of AI YORAM (JERRY) WIND, MARGHERITA PAGANI, AND STACEY LYNN SCHULMAN

2

Could Artificial Intelligence make us humans more creative?

24

MARGHERITA PAGANI AND RENAUD CHAMPION

PART TWO

From individual creativity toward business creativity 3

When Artificial Intelligence systems help to inspire creative new venture ideas

45

47

NATHAN SORIN AND MARGHERITA PAGANI

4

How AI can foster business creativity MARGHERITA PAGANI AND RENAUD CHAMPION

65

viii Contents 5

Artificial Intelligence and creativity in marketing: a proposed typology and new directions for academia-industry collaborations

82

NISREEN AMEEN, GAGAN DEEP SHARMA, AND SHLOMO Y. TARBA

6

Toward AI-enabled support for creative thinking about business models

99

MARK DOWSETT, NEIL MAIDEN, AND CHARLES BADEN-FULLER

7

Conclusions and future directions

125

MARGHERITA PAGANI AND RENAUD CHAMPION

Appendix: Legal issue of AI for business creativity

129

OLIVIER LASMOLES

Index

132

About the authors

Nisreen Ameen is Senior Lecturer in Digital Marketing at Royal Holloway, University of London, UK and the Co-Director of the Digital Organisation and Society Research Centre. She is also currently serving as Vice President of the UK Academy of Information Systems (UKAIS). She is Associate Editor for Information Technology and People, Computers in Human Behavior, and the International Journal of Consumer Studies. Ameen has also served as a guest editor for special issues in various top-ranked journals such as Information Systems Frontiers, Computers in Human Behavior, Psychology and Marketing, Industrial Marketing Management, Journal of Business Research, The Service Industries Journal, and International Journal of Consumer Studies. Charles Baden-Fuller is Centenary Professor of Strategy and International Business at Bayes Business School, City, University of London, UK and a visiting fellow at the Wharton School, University of Pennsylvania, US. He has made major contributions to knowledge in the field of strategy: in particular why firms become mature and how they can be rejuvenated, the theory and practice of alliances, the role of cognition in management competitive dynamics, and what are business models and how they work. He has also had a significant impact on policy through his writing and his speaking. His honors include being elected a fellow of the British Academy, The Strategic Management Society, and The British Academy of Management. Renaud Champion is Founder of PRIMNEXT, an early-stage investment company specialized in valuing disruptive technologies, scaling-up companies operating in advanced robotics and artificial intelligence. He is an advisor to the European Commission on its strategy for AI, Data and Robotics. He created the Artificial Intelligence in Management Institute and as an investor, he founded Robolution Capital, the first VC fund dedicated to AI and robotics.

x About the authors Gagan Deep Sharma currently works as a professor in management at Guru Gobind Singh Indraprastha University, New Delhi, India. He holds a PhD in management and a master’s in philosophy and commerce. He has an academic experience of over 18 years. He also holds the position of Associate Director, Office of International Affairs of the University. He is an associate editor of Qualitative Research in Organizations and Management, Journal of Public Affairs, and Corporate Governance. Sharma is a subject editor for Social Sciences and Humanities Open. He is a guest editor for Resources Policy;Venture Capital; Energy Sources; Part B: Economics, Planning, and Policy; Annals of Financial Economics; World Review of Entrepreneurship, Management, and Sustainable Development; and International Journal of Economic Policy in Emerging Economies. Mark Dowsett is a PhD candidate at the Bayes Business School of City University of London, UK. His research sits at the intersection of human creativity and technology, with a particular interest in the antecedents and determinants for the adoption and use of human-centered artificial intelligence. Igor Jablokov is CEO and Founder of Pryon. Named an “Industry Luminary” by Speech Technology Magazine, he previously founded industry pioneer Yap, the world’s first high-accuracy, fully automated cloud platform for voice recognition. After its products were deployed by dozens of enterprises, the company became Amazon’s first AI-related acquisition. The firm’s inventions then served as the nucleus for follow on products such as Alexa, Echo, and Fire TV. As a program director at IBM, Jablokov led the team that designed the precursor to Watson and developed the world’s first multimodal Web browser. He was awarded Eisenhower and Truman National Security fellowships to explore and expand the role of entrepreneurship and venture capital in addressing geopolitical concerns. He serves as a mentor in the TechStars’ Alexa Accelerator, was a Blackstone NC Entrepreneur-In-Residence (EIR), and founded a chapter of the Global Shapers, a program of the World Economic Forum. Jablokov holds a B.S. in computer engineering from Pennsylvania State University, US, where he was named an Outstanding Engineering Alumnus, and an MBA from University of North Carolina, US. Olivier Lasmoles holds a doctorate in private law from the University of Paris I Panthéon-Sorbonne. He works in the fields of maritime law (maritime safety, marine environmental law), criminal law, and cyber law (AI, blockchains, cybersecurity). In addition, he is a National Defence Institute of Higher Education (IHEDN) auditor, member of the AR11 Executive Committee and member of the Union-IHEDN Cybersecurity Commission.

About the authors xi Neil Maiden is Professor of Digital Creativity at the Bayes Business School at City, University of London, UK, and Director of the National Centre for Creativity enabled by AI funded by Research England. His research interests include uses of artificial intelligence to augment human creativity in professional work. Margherita Pagani, PhD, HDR, is a full Professor of Digital and Artificial Intelligence in Marketing and Director of SKEMA Center for Artificial Intelligence at SKEMA Business School. Associate Dean of SKEMA AI School for Business. She also serves as an Advisor for the European Economic and Social Committee (EESC) and is associate editor for Micro & Macro Marketing. Her research interest focuses on the impact of AI on consumer behavior and on digital ecosystems. She has previously held positions at emlyon business school, Bocconi University, and MIT’s Sloan School of Management, and has received global recognition for her research and publications through various awards. Stacey Lynn Schulman is an award-winning executive at the intersection of marketing and analytics. As the founder of Hi: Human Insight, Schulman is laser-focused on the human aspect to data insights and strategy. She is highly experienced as both a new media and traditional researcher, having conducted TV, print, radio, cable, and digital research for global, national, and local US media entities (CBS, Turner Broadcasting, iHeartMedia, Sports Illustrated) as well as for global media agencies (DMB&B, Bozell, TN Media, Initiative). She has been routinely quoted in trade and consumer media outlets, appearing regularly on CNN, CNBC and FOX News Channel to discuss media trends. Nathan Sorin is a PhD candidate at SKEMA Business School, France. His research focuses on AI-enabled business models as well as the influence of generative AI on entrepreneurial creativity. He holds a “Grande Ecole” MSc from Emlyon Business School, France, and a master’s degree in research in management from University Lyon III, France. Shlomo Y. Tarba is Chair (Full Professor) of Strategy and International Business, University of Birmingham, UK. His research interests include M&A, agility, and AI. His papers are published in Journal of Management, Journal of Product Innovation Management, Journal of Organizational Behavior, Academy of Management Perspectives, and Journal of Service Research. Yoram (Jerry) Wind is Professor Emeritus and Professor of Marketing at Wharton Business School, US. He has a PhD from Stanford and has been active in research, publication, and lecturing. He founded the Wharton “Think Tank” – The SEI Center for Advanced Studies in Management and has directed it for 30 years. He has led the development of the Wharton

xii About the authors Executive MBA and the Lauder Institute and has published over 300 articles and 30 books. He has received five major marketing awards and was inducted into the inaugural group of AMA Fellows. He is a member of various company and nonprofit advisory boards, a trustee of the Philadelphia Museum of Art, and a cofounder of Reichman University, Israel. His current research focuses on marketing, creativity, innovation, and AI for customer engagement. He is a cofounder of the Reimagine Education global competition and annual conference.

Foreword The intersection of AI and creativity Igor Jablokov

The Age of Artificial Intelligence (AI) is upon us. It has wrought seemingly equal measures of excitement along with concern bordering on panic. The collective thinking on AI seems to simultaneously inspire endless possibilities or, more pessimistically, existential doom. Perhaps there is another overlooked angle to consider. What if AI can act as a great emancipator of our latent creative talents? What if it can play the role of an inspiring muse in unlocking our collective potential? As Margherita Pagani and I wrote in an article for Harvard Business Review (HBR)1 with the headline “Enabling a New Generation of Creative Leaders”: Applying AI to organizational creativity can lead to the development of new products, ideas, collaboration methods . . . even new ways of thinking. What’s more, there is a modern-day version of natural selection at play in which organizations that adopt AI to construct a new kind of creative “connective tissue” will be the ones that evolve to launch the next generation of expansive, modern leaders – and innovations. Every organization is hunting for new ideas to pursue. But, as humans, we are confronted with the scarcity of a key resource: time. Most management models are more focused on squeezing every ounce of productivity out of each working day as opposed to following the Google model of giving each employee 20% of their time to “think outside the box”.2 That’s especially true during uncertain economic times, when the focus on the health of an organization’s bottom line becomes paramount. The focus becomes one of “sticking to your knitting”, as the saying goes. Yet, there’s real risk in chasing the status quo. To paraphrase a quote from John Chambers, the former chairman and CEO of Cisco Systems, you can get in trouble if you continue to do the right thing for too long.3 How many times have we seen the same story unfold, generation after generation, where the stable incumbent gets unseated by a sucker punch they never saw coming?

xiv Foreword Blockbuster, IBM, Kodak – all iconic brands who did the right thing for too long. Adaptation is mandatory for any organism to survive and thrive in its ecosystem. In the same way some biologists say that most of our cells are exchanged every seven years, the conditions for operating our respective ventures always remain in flux. More organizations need to embrace the sense of creative self-destruction that Apple does. You need to be willing to cannibalize your cash cows. But how can you identify and stay ahead of that next disruption heading your way if you don’t have sufficient time to think about and process the situation? This is where I believe AI will play a key role in the future. In the past, computing was constrained to technically competent computer scientists, mathematicians, and software engineers who understood how to speak the language of computing. Now, the tables have turned. As we continue to improve the interfaces between humans and machines, we can unlock new capabilities at scale from everyone – especially when it comes to creativity. Creativity defines our species. We are the ultimate progeny of those who innovated solutions to existential problems in the far-reaching past. And it is entangled with our emotions, as we often forget those are leveraged first in decision-making. As we reduce the distance between knowledge and people of all backgrounds, we can increase the velocity at which they experiment. By leveraging AI in the workplace and using it to consume and process data at a scale no human could, we can open the aperture wider in ways that will allow us to see further than beyond what we thought possible. And yet, new questions will be raised as access to AI becomes democratized. Just as fears arose when machines began to automate labor, what happens when we can automate some aspects of creativity? Consider more recent examples of the intersection of AI and the art world. A debate is now raging about whether something generated by a computer can even be considered art. You can use a tool such as Stable Diffusion where, by simply prompting it with text directions, you can generate representative images. Enter in a few simple directions – a dark room, a group of dogs wearing 1920s-era clothing, all of them playing poker – and you can produce a piece of familiar art. Similarly, one can employ the AI engine behind OpenAI’s ChatGPT to compose blog entries, articles – and even author books. (In case you were wondering, no, this forward was written by an actual human). Yet, works like these are not a product of magic. They did not materialize out of thin air. They are the regurgitated output of an algorithm interacting with a massive model that digested countless works of art composed by organic dreams and ingenuity. Viewed this harshly, these AI “artists” are nothing more than glorified plagiarists. I believe the jury remains out on the ultimate judgment of whether AIgenerated art is, well, art. Where does uniqueness in any of our endeavors start

Foreword xv and end? But I think there is a far more interesting topic to delve into – how building AI is a creative pursuit in and of itself. Think about the relationship between a writer who pens a brilliant stage play and the actor who performs the scripted role on the stage. No one would argue that both the writer and the actor are fully expressing a range of creative talents. But the beauty results from their collaboration. Without a script, the actor would be left to randomly ad-lib on the stage. Without the actor to perform, no one could see the world the playwright imagined beyond the page. They work in harmony to bring a work of art fully to life. The relationship between the human AI scientist and the AI algorithm is similar. Without a human to guide it, an AI would be aimless, or worse, dangerous. We have seen what can happen when algorithms built with inherent biases run amok. But, when paired with a great script, something beautiful and profound can result. That’s because computers can execute at a scale and with a speed no human can ever hope to duplicate. A mundane task of sorting a group of photos based on their colors, themes, or time stamps that would take a human many hours to complete, for instance, can be done by a machine in less time that it takes us to inhale our next breath. When coupled with a creative human brain, the possibilities of this partnership can be limitless. If we want to harness the vast capabilities of our AI actors, our script writers need to dream big – and remain open to inspiration wherever it may arise. Consider that even the famed European painter Vincent Van Gogh was himself inspired by works of art such as block woodcuts from Japan. That is why our AI scientists must also be curious and well-rounded about a wide range of aesthetics and art forms and combine them with fields that vary from biology to psychology to astronomy and materials science. Ample research shows us that teams composed of individuals with diverse abilities outperform ones with homogeneous abilities. Yet, too many people maintain a prevailing thought that computer science is antiseptic, bland, and unapproachable. The opposite is true. It is a cauldron of creativity. Art inspires science and science inspires art. Many academic departments now push STEM (science, technology, engineering, math) to be expanded into STEAM, by including an “A” for art. Think less salt-of-the-earth style engineering and more Disney-esque cross-discipline “Imagineering”. My own history as an AI practitioner was inspired by my relationship with the arts as a child. Growing up under the guidance of artistic parents, they spared no expense, even when they could not afford to, on classes in computers, dance, history, languages, music, and painting for my siblings and me. Those lessons linger with me today and profoundly inspire and shape my own work in the field. AI in this lens is viewed as a canvas for broadening one’s self-expression to a world stage. For all human history, writers have conceived of alien technologies and imaginary worlds. Then, when some of us, especially as children, voraciously consumed these stories, we saw ourselves visiting these distant and strange

xvi Foreword places by hopping into a rocket ship or entering a transporter booth. As soon as anything is imagined like this, it becomes instantly possible. It can happen because some of us had early designs on transforming “science fiction” into “science fact” because we believe it will introduce a world with less strife and more opportunity for all. If we wish to leverage the full power of AI, both inside organizations and outside in the greater world, we need the script writers of the future to fully tap into their own creativity first. Only then can we uncover and prioritize the most daunting challenges to pursue with our work. What kinds of stories can we put forth to inspire others? Working together, AI scientists and their AI actors can tackle even the biggest problems we collectively face – inside organizations and beyond. Even small changes can begin to change the course of history. As we wring our hands over a world filled with war, pandemics, economic distress, and climate change, we need our end users to imagine the kinds of scripts that can lead us beyond seeing AI as a mere marketing optimization tool or even a destroyer of worlds. Rather, it is time to embrace AI as an ally that can help make tomorrow’s world better than it was yesterday. That’s precisely the kind of inspiring mission Margherita Pagani and Renaud Champion have undertaken with this exploration. It is a continuum of work that will enable organizations of all sizes to go deeper into the 21st century with consequential outcomes in their hearts and minds. To quote again from Margherita’s and my joint HBR article: Organizations and their leaders must pay more attention to how AI can help their workforce maximize their humanness – not replace it – and nurture the next wave of leaders who default to this way of thinking. In the pages that follow, you will learn more about how AI systems can be employed to help unleash creativity in ways that enable us to tackle today’s major problems in the workplace and beyond. This is a journey we must all undertake together, and these pages will serve as an embarkation point as we push into the next stages of our shared future. I am doing my part. In my time at IBM, our multimodal research team experimented with the precursor to Watson while simultaneously bootstrapping Apple’s and Google’s early efforts in AI. Then, I founded a private company, Yap, that became Amazon’s first AI-related acquisition that led to the birth of Alexa. Many thought our original goals for that venture were impossible, discounting that we formed a multidisciplinary team, unusual for that era. Now, at Pryon, we are working on transforming our workplaces by unlocking the treasure trove of static content and know-how that wastes away in sprawling enterprises into an interactive knowledge fabric that drives more immediate business value.

Foreword xvii At The Atlantic’s Future of Work conference in 2019, I commented that brands should operate more like intelligence agencies to survive this century. Now it is abundantly clear they also need to learn how to finger paint to thrive in it. Only by more fully integrating our left and right brains can we realize our potential. Who knew that AI could become that bridge? When we were children, all of us did. Igor Jablokov Founder & CEO, Pryon

Notes 1 Pagani Margherita, Jablokov Igor (2022) “Et si l’IA pouvait aider à former une nouvelle génération de leaders créatifs?" Harvard Business Review France www. hbrfrance.fr/chroniques-experts/2022/10/50146-et-si-lia-pouvait-aider-a-former-unenouvelle-generation-de-leaders-creatifs/ 2 Google Co-founders Larry Page and Sergey Brin highlighted the idea in their 2004 IPO letter. 3 “Former Cisco CEO John Chambers is trying to change the world”, Forbes (2018) https://www.forbes.com/sites/peterhigh/2018/12/03/former-cisco-ceo-john-chambersis-trying-to-change-the-world/

Introduction Margherita Pagani and Renaud Champion

We are living today in an era characterized by volatility, uncertainty, complexity, and ambiguity, and businesses need to pass through a real metamorphosis, transformation, and change. This metamorphosis cannot be described as a simple transition, but it is a real mutation. We may compare this metamorphosis to the biological process that transforms a caterpillar into a beautiful butterfly. The process starts with a deep impulse at the cellular level of the caterpillar. Some cells called imaginal discs remain dormant until they suddenly awaken when the time is right. These cells are so different from the cells of the caterpillar that the caterpillar’s immune system rejects them attacking them as invaders, but imaginal cells persist and multiply within the caterpillar. These new cells resonate at a similar frequency, communicating with each other and self-organizing to overwhelm the caterpillar’s immune system. Next, the caterpillar goes through a beautiful metamorphosis to become a butterfly. In this book, we explore these imaginal cells fostered by Artificial Intelligence (AI) driving the business metamorphosis. The question we want to address is, “How can Artificial Intelligence help managers change dimensions to cope with today’s major issues in a creative way?” Business creativity is a new way of thinking that inspires, challenges, and helps people to find innovative solutions and create opportunities out of problems. Robotics, AI systems, blockchain, global digital platforms, and autonomous systems have begun to transform and aid creative and complex processes inside companies. They challenge what we believe to be “creative thinking” as the ability to see things from more than one perspective and question the existing working models. A wide range of emerging technologies including 5G, quantum computing, open-source code, and deep learning are expanding and accelerating the capabilities offered by Artificial Intelligence. Powerful computer applications today allow manipulating and fabricating digital objects in unimaginable ways, developing computing creativity. The key challenges in all these AIenabled tools that require high levels of creativity are involving humans specifically for semantic, high-level tasks that are simple for them but extremely DOI: 10.4324/9781003287582-1

2 Margherita Pagani and Renaud Champion difficult for the machines and leveraging machines and AI systems for tasks that are tedious for humans. If AI systems can most effectively augment some human capabilities and have advanced to a point where they can autonomously generate ideas, concepts, or rough drafts, they still cannot replace an individual’s human ability to foster introspection and creativity (intrapersonal intelligence), which is one of the most striking capacities of humans displayed in our thoughts and behaviors. AI systems serve as tools humans can use to augment their creative ability, and they offer a new kind of superpower for the next generation of creative employees to rethink tough problems in fresh ways. Inside a business organization, creative employees enjoy challenges requiring an innovative approach. They bring fresh concepts to the table, are not afraid to challenge the way things are done, and find a better way to make the world work. That’s why companies need human skills such as creativity (Mehta & Zhu, 2016), even in an age of automation and AI. Consumers engage in creative behaviors to satisfy their needs and solve consumption-related problems (Burroughs & Mick, 2004). Meanwhile, many businesses achieve success because of their consumers’ and employees’ abilities and desires to be creative (Mehta et al., 2012). There’s no doubt that the boundaries of AI’s role in creative endeavors will be pushed further, but even if AI systems can certainly offer many benefits serving as a smart, efficient, and inspirational assistant, we believe they are not only a tool, but they can also inspire the human soul of creativity and help organizations in their metamorphosis.

The business impact of creative AI A study by McKinsey (2021) found that 77% of senior company leaders surveyed believe that creativity is a crucial driver for growth. Even if AI is already extensively implemented to assist in completing routine tasks increasing efficiency, creative AI will play a role in tasks that require human comprehension, generating a hike in productivity. This is confirmed by a recent study conducted by Nasdaq (2021) on a sample of 2,935 respondents that showed that businesses using AI creativity tools for video ad creation saw an average two times increase in return on advertising spend (ROAS) over campaigns without AI creative support and up to a seven times increase for some campaigns in the study. Recommendation systems applied to marketing processes use AI and machine learning to comb through real-time, contextually relevant data about customers’ characteristics and interactions to suggest a smaller, more manageable set of “likely” options that marketers can use for more personalized and creative customer interactions (Mustak et al., 2021; Longoni & Cian, 2022). AI systems were found to help the design of new products or inspire

Introduction 3 marketers’ decisions also through social media mining (Kietzmann et al., 2018), real-time price optimization (Davenport et al., 2020), sentiment analysis (Ma & Sun, 2020), and customer churn analysis. We also saw a significant increase in the use of AI-driven chatbots, drones, algorithms, robots, and digital voice assistants to help customer assistance and personalize customer experiences. Innovative businesses will capitalize on creative AI to improve creative thinking activities and find solutions to problems humans can’t solve. The same phenomenon happens when we consider AI systems in the supply chain, finance, or R&D. In all these examples, artificial intelligence systems not only allow to simplify things or make processes more efficient, but they also allow businesses to project, amplify, detect weak signals, and creatively transform themselves.

Aims and scope In this book, we specifically explore how AI systems applied to complex and creative tasks can help managers change dimensions to cope with today’s major issues in a creative way. In doing this, we aim to explore how Artificial Intelligence influences the human soul of creativity, stimulating the ability to generate new ideas or help managers to find new solutions. We argue that to work in collaboration with machines in AI-augmented work environments for complex and creative tasks, Managers of the Future (MoF) need to develop a set of new critical skills that combine technical expertise with new human abilities. Companies adopting AI systems may benefit from optimizing technical collaboration between humans and Artificial Intelligence. By connecting technology to human values in the respect of ethical concerns, they will efficiently leverage the uniqueness of human creativity to develop new human abilities. The book addresses managers and scholars interested in discovering the potential of Artificial Intelligence for business and how to boost workforce creativity with AI. This book is divided into six chapters organized around two main parts: Part One: Artificial Intelligence and creativity, Part Two: From individual creativity toward business creativity. In Part One, we lay the foundations and explore how Artificial Intelligence may foster individual creativity and what the challenges are for creative managers working in AI-enabled environments. Chapter 1 “Creativity and innovation in the age of AI” aims to explore the role of AI in enhancing human creativity. The chapter focuses specifically on various tools and approaches used to enhance the human ability to generate creative options and examines how AI can enrich these approaches, leading to the generation of more and better creative options and the prioritization of those options. It concludes with guidelines for the use of AI as an approach

4 Margherita Pagani and Renaud Champion to identify and evaluate creative options as well as a multiplier of approaches that enhance creativity. Chapter 2 “Could Artificial Intelligence make us humans more creative?” specifically considers how Artificial Intelligence systems may foster individual creativity. After providing an introduction to computational creativity and describing the different levels of observed creativity of the AI systems, the chapter specifically considers creative industries and how artists are using Artificial Intelligence systems in their creative endeavors. Three main ways in which AI can influence the creative process are identified. In Part Two, we focus on creativity at the company level and explore the concepts of entrepreneurial creativity and business creativity. We specifically investigate how managers are using this technology to find innovative solutions and create opportunities out of problems. Chapter 3 “When Artificial Intelligence systems help to inspire creative new venture ideas” empirically investigates how generative Artificial Intelligence (AI) systems can foster idea generation during the new venture creation process. After providing an overview of entrepreneurial creativity, the chapter describes generative AI systems and their use for new venture idea generation stimulating cognitive flexibility. Chapter 4 “How AI can foster business creativity” explores how AI systems can inspire creativity inside a company. The chapter presents a theoretical review of the concept of business creativity and the impact of AI on creative products, processes, and collaborations. Grounded on the concurrent engineering (CE) framework and based on interviews with key leaders and AI managers, the chapter provides a taxonomy and discusses implications for management seeking to foster creativity using AI-driven innovation in the workplace. Chapter 5 “Artificial Intelligence and creativity in marketing: a proposed typology and directions for academia-industry collaborations” identifies a typology of the key areas of marketing requiring creativity and the impact of AI (high/low) on these areas (e.g., viral marketing, new product design, advertising, social media marketing, logo design, service design). The chapter provides an outline of key avenues for future research on AI and creativity in marketing from a business perspective. Finally, Chapter 6 “Toward AI-enabled support for creative thinking about business models”, using a design science approach, presents a prototyped AI tool to support business owners to think more creatively about business models

The promise of this book The book aims to provide in-depth analyses and studies of applications of AI to real-world problems requiring creativity and provide guidelines to

Introduction 5 managers on how to foster creativity inside a company. Each chapter presents an original, critical, and accessible account of the current state of the debate in its domain. The uniqueness of this book is the focus on business creativity, a subject of great importance but still not addressed by existing publications. Moreover, the book, using a multidisciplinary perspective, depicts the interests of a wide spectrum of practitioners, students, and researchers alike who are interested in identifying the value generated by AI systems in management and discovering opportunities and challenges. We are deeply grateful to the key leaders and experts who shared their vision, insights, and daily experience through interviews detailed in Chapter 4. Specifically, thanks go to Igor Jablokov (CEO, Pryon), Bruno Bonnell (General Secretary for Investment, France 2030), Pierre Collinet (PhD/ MD Gynecologist Surgeon), Luc Julia (Chief Scientific Officer, Renault Group), Silvano Sansoni (General Manager, Global Digital Sales, IBM). This book represents for us a further step in a journey started five years ago around the impact of Artificial Intelligence for business on/in the world, which has led to several actions: publications (book, articles); organization of seminars with key experts in the world; academic initiatives; investments in AI start-ups; international implications in academic and professional communities; organisation of conferences, and educational programs aimed to responsively and ethically improve the (business) world thanks to AI. We wish to thank those who have helped and encouraged this journey and all the outstanding contributors to this book: Yoram (Jerry) Wind (Wharton – US), Stacey Lynn Schulman (Hi: Human Insight), Nathan Sorin (SKEMA Business School), Nisreen Amen (Royal Holloway – University of London – UK), Gagan Deep Sharma (Sing Indraprastha University – India), Shlomo Y. Tarba (The University of Birmingham – UK), Mark Dowsett, Neil Maiden, and Charles Baden-Fuller (Bayes Business School, City University of London – UK). Looking forward to the next step!

References Burroughs, J. E., & Mick, D. G. (2004). Exploring antecedents and consequences of consumer creativity in a problem-solving context. Journal of Consumer Research, 31(2), 402–411. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(7553), 1–19. Kietzmann, J., Paschen, J., & Treen, E. R. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263–267. Longoni, C., & Cian, L. (2022). Artificial intelligence in utilitarian vs. hedonic contexts: The “word-of-machine” effect. Journal of Marketing, 86(1), 91–108.

6 Margherita Pagani and Renaud Champion Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computer power to human insights. International Journal of Research in Marketing, 37(3), 481–504. McKinsey. (2021, June). Getting tangible about intangibles: The future of growth and productivity? survey of 860 executives by McKinsey Global Institute. https://www. mckinsey.com/~/media/mckinsey/business%20functions/marketing%20and%20 sales/our%20insights/getting%20tangible%20about%20intangibles%20the%20fu ture%20of%20growth%20and%20productivity/getting-tangible-about-intangiblesthe-future-of-growth-and-productivity.pdf Mehta, R., & Zhu, M. (2016). Creating when you have less: The impact of resource scarcity on product use creativity. Journal of Consumer Research, 42(5), 767–782. Mehta, R., Zhu, M., & Cheema, A. (2012). Is noise always bad? Exploring the effects of ambient noise on creative cognition. Journal of Consumer Research, 39(4), 784–799. Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389–404. Nasdaq. (2021, January 12). Pencil doubles returns for brands with next-gen ads. Nasdaq (Survey of 2935 respondents from Jun 9 to July 19, 2020). https:// www.nasdaq.com/press-release/pencil-doubles-returns-for-brands-with-nextgeneration-ads-created-by-ai:-pencils

Part One

Artificial Intelligence and creativity

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Creativity and innovation in the age of AI Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman

Introduction Creativity, the spark that lights the flame of progress, The source of all that’s new and bold and bright, A wellspring of ideas that never seem to cease, A force that drives us forward, ever into the night. Without creativity, we’d be stuck in stasis. Trapped in a world of sameness and of routine, But with its power, we can break free from the static And forge a path to greatness, as we’ve never before seen. So let us embrace our creative spirit, And let our minds run free and wild, For in the realm of imagination, We can achieve things that once seems only a child’s. So let us nurture and cultivate Our creativity, and let it guide us, To a future that is bright and full of hope, A world where anything is possible, if only we choose. This sonnet was written by ChatGPT in response to a query: “can you write a sonnet about the importance of creativity?” The content is accurate and illustrates one of the many capabilities of Artificial Intelligence (AI). Yet, what this AI-generated response left out is the increasing role of AI in enhancing creativity and, thus, in stimulating and facilitating innovation. AI, in and of itself, has been a perpetual muse for human advancement through its incorporation into one of humanity’s most powerful skills – storytelling. Characters such as Star Trek’s Mr. Spock (1960s) and Commander Data (1990s reboot), as well as the wisecracking car, Kit, in the Knight Rider series (1980s) or Samantha in the movie Her (2010s) have all personified AI in popular culture. The wonder and potential of an AI-enabled future has even been reflected in the creation of EPCOT, the “Community DOI: 10.4324/9781003287582-3

10 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman of Tomorrow” theme park envisioned by American animator and Imagineer Walt Disney. The creation of AI and AI applications requires enormous creativity and technical competences. But once AI tools exist, they can significantly enhance the ability to create creative products, service solutions, and engaging experiences. However, AI’s role in creativity and innovation has only recently been explored, and it is now considered a vital and rapidly growing sector. Innovation is not only about creating something that is “new” but also something that is useful and has a market. It allows for the development of new products, services, and experiences that can improve people’s lives and help to solve important social and economic problems. Progress can also lead to increased innovation by creating new opportunities and a better overall environment for new ideas to emerge (Nelson, 2003). AI, therefore, can act as a powerful engine that not only inspires innovation but also enables and accelerates it. This chapter aims to highlight the various roles AI systems can play in increasing the creativity and innovativeness of individuals, organizations, and society. To do so we first offer eight accepted guidelines for fostering innovation. The rest of the chapter focuses on identifying the roles AI can play in enhancing and accelerating each of the guidelines.

Guidelines for enhancing innovation and generating innovative solutions Innovation is crucial for businesses, nonprofit organizations and even governments to leverage the changing business trends, stay ahead of the competition, and meet the evolving needs of their customers and stakeholders. However, innovating can be a challenge for many organizations. The following are eight guidelines for fostering innovation within an organization: 1 Challenge the status quo and mental models of your industry or target industry. There are many approaches to challenge one’s mental models (see for example Wind & Crook, 2006) but still too many legacy firms accept the status quo. And this is despite the known fact that breakthrough innovations that change our lives, like Google, Uber, Apple, Amazon, and others, require changing the mental models of their industries and lead to enormous rewards. 2 Engage with your current and future customers to understand their current and future needs and objectives. 3 Generate innovative solutions under alternative scenarios that reflect future customer needs and objectives. This might include innovative solutions based on advances in science and technology; the results of

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analyzing the emergence of new competitive strategies from current and new competitors; the changing social trends; the changing global geo, political, economic, and regulatory trends; the shifting business model from shareholder to stakeholder orientation; and the interdependencies of these trends. Set objectives for innovation initiatives that encompass all value creation areas. These include exploring new products, services, and experiences; positioning for target segments; developing omnichannel distribution and promotion; investigating business and revenue models; and focusing all of them on the pursuit of your and your stakeholders’ short- and long-term objectives and vision. Expand the scope of your innovation to encompass the entire organizational architecture. This should include processes, capabilities, structure, technology, creativity tools, performance measures, incentives, infrastructure, and whatever is required to orchestrate all the internal and external capabilities in the creation and delivery of value through all touchpoints to all stakeholders. Mobilize and orchestrate a portfolio of innovators. Design and implement an innovation strategy that leverages a balanced and agile portfolio of internal teams, open innovation, M&A and strategic alliances, client innovation teams, and the networks of all involved. Assure effective orchestration of the entire innovation process. Lead innovation efforts as an “enlightened orchestra conductor”, who inspires and empowers team members to perform common goals as part of a coherent orchestra that strives for perfection. Adopt continuous experimentation. Create an agile innovative organization that focuses on continuous iterations and experimentation. An organization that does not accept the old belief that one has to choose among speed, quality, and cost. True innovation allows the achievement of all three – quality, speed, and cost.

The role of AI in enhancing innovation AI impacts all aspects of our lives – the way we live, play, work, and communicate – and all industries and business functions. While it may seem like a new technology, AI has a long history, as demonstrated by the Stanford Human-Centered Artificial Intelligence (HAI) video “AI at Stanford 1962–2022”.1 This history shows how AI (from its early programs playing chess, computer-generated music, early robotic applications, medical expert systems, and even the first autonomous driving car) has always played a critical role in creating innovative products, services, and experiences. Let’s turn now to illustrative examples of the role AI plays in enhancing the eight innovation guidelines featured in the previous section.

12 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman 1. AI for challenging the status quo and mental models of your industry or target industry Challenging the status quo and the prevailing mental models of an industry can be a difficult endeavor. Machiavelli noted this over 600 years ago, stating that: There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in introduction of a new order of things. Because the innovator has for enemies all those who have done well under the old conditions, and lukewarm defenders in those who may do well under the new. While the rewards for the brave breakthrough innovators can be significant, as evidenced by leading companies in the new economy, many legacy firms tend to maintain the status quo. AI and machine learning can be used to automate the process of creating and analyzing scenarios, allowing managers to rapidly explore a wide range of possibilities. Algorithms can be trained on historical data and used to generate predictive models that help managers understand the likelihood of different outcomes based on different assumptions. This can be particularly useful in industries with high levels of uncertainty, such as the technology or financial sectors. For example, Goldman Sachs utilizes machine learning to build predictive models that inform investment decisions and risk management strategies (EMERJ, 2023). Netflix, Uber, and Spotify are all examples of companies that have used AI to change the mental model of their respective industries and create new ones. Netflix, a streaming media company, has used AI to disrupt the traditional television and movie industries by changing the mental model of how content is consumed and distributed. One example of how Netflix has incorporate AI is through its recommendation system, which uses machine learning algorithms to personalize the content that is suggested to each individual user. This has led to a shift in the way that television and movies are consumed, as viewers are now able to access a vast library of content and are no longer limited to the programming that is scheduled by traditional cable and broadcast networks. Netflix has also used AI in the production of its own original content and data analysis to determine the types of shows and movies that are likely to be popular with its audience, and it has invested heavily in producing its own content that fits within these parameters. This has led to the creation of a new industry as other companies have followed suit and begun to produce their own original content. In the case of Uber, the company has used AI in the form of machine learning algorithms to optimize its ride-hailing service. For example, Uber’s algorithms can predict demand for rides in real time and adjust the pricing accordingly as well as match riders with the nearest available

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driver. The implementation of AI at Uber has resulted in a significant alteration of not only the opportunities available to drivers utilizing the platform but also the convenience experienced by riders. Furthermore, this has had a significant impact on the lifestyles of those who have chosen to utilize Uber as an alternative to owning a personal vehicle. Additionally, it is notable that Uber has also brought about a transformative shift within the taxi industry, leading to the gradual emergence of competing for app-based services. This has resulted in a new industry, in which individuals can earn money by providing services such as ride-hailing or delivery on a flexible, on-demand basis. Similarly, Spotify has used AI to change the way that music is consumed and distributed. The company’s recommendation algorithm uses machine learning to personalize the music that is suggested to each individual user. Additionally, Spotify has also used AI in its efforts to create new music through its “artist discovery” feature, which uses machine learning to identify emerging artists. This has led to a shift in the way that music is discovered and consumed and has also created new opportunities for independent artists. Spotify also uses data on listening habits to personalize its advertising and sponsorships. In summary, Netflix, Uber and Spotify, among others, have used AI to change the mental model of their respective industries and have created new opportunities for individuals and companies through the use of on-demand services, personalized recommendations, and original content production. 2. AI for a better understanding of consumers and their current and future needs AI has emerged as a powerful tool in marketing and consumer research, enabling businesses to gain valuable insights into customer perceptions, preferences, and behaviors. The application of AI in customer analytics allows for the analysis of customer data from various sources, such as social media, online reviews, and purchase history, to gain a deeper understanding of customer needs and purchasing decisions. AI enhancements have been applied to the traditional marketing research discipline, leading to automated research software that can learn and adapt to client feedback in real-time. This has allowed for the identification of new or more significant insights from data that has already been collected as well as the ability to quickly find and vet study respondents. Furthermore, AI tools have been used to improve surveys in real time, optimize questions based on feedback, increase participation response, and predict poor quality data (and subsequently improve datasets). The utilization of AI technology to drive new marketing and consumer research services has been implemented by several companies across various

14 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman industries. One example is Emotient, a company that employs AI-powered facial recognition technology to analyze consumers’ emotional reactions to advertising and other marketing materials. This technology is able to detect facial expressions that indicate emotions such as happiness, sadness, and surprise, thus allowing marketers to comprehend how consumers are responding to their campaigns. Another example is Affectiva, which also utilizes AI-powered facial recognition technology to analyze consumers’ emotional reactions to advertising and other marketing materials, but in addition, uses speech and voice analysis to understand consumers’ emotional responses. Cogito uses AI to analyze customers’ emotional state during phone calls and customer service interactions and provide real-time guidance to the agent, which can improve customer satisfaction and loyalty. These are a few examples of companies that are using AI to drive new marketing and consumer research services. By utilizing AI-powered technologies such as facial recognition and speech and voice analysis, companies can better understand consumers’ emotional reactions to advertising and other marketing materials, thus improving the effectiveness of their campaigns. The utilization of AI to predict future needs and behaviors in real time, to provide a more personalized and efficient customer experience, has been implemented by several companies across various industries. One example is Salesforce, which employs Einstein AI, a machine learning algorithm, to predict which leads are most likely to convert into customers and which accounts are at risk of churning, thus helping sales teams prioritize their outreach efforts. Another example is Domino’s Pizza, which utilizes AI to analyze customer data such as purchase history and online browsing behavior to create personalized product recommendations and targeted marketing campaigns, thus improving the overall ordering and delivery experience for customers. Tesco, a British supermarket giant, uses AI to predict customer demand for specific products and optimize inventory levels in real time, reducing waste and increasing efficiency, which improves the overall shopping experience for customers. Similarly, Starbucks employs AI and machine learning to analyze customer data and predict future demand, helping the company to optimize inventory levels and improve the efficiency of its supply chain, thus improving the overall experience for customers. All these companies are utilizing AI to predict future needs and behavior in real time to provide a more personalized and efficient customer experience. The use of AI in this manner can assist companies to improve their operations, increase efficiency, and provide better customer service. In summary, improvement in marketing and consumer research, modeling, and analytics due to AI will lead to better understanding of consumer needs, which will inspire the creation of better products, services, and experiences.

Creativity and innovation in the age of AI 15 3. AI for generating innovative product and service solutions under alternative scenarios AI is being used by various companies to generate innovative and, at times, truly breakthrough products, services, and experiences in two ways: (a) by being the core of the innovative offerings and (b) by helping generate innovative new offerings. (a) Innovative solutions feasible only because of AI Companies such as Netflix and Spotify have implemented AI to generate innovative solutions that exist because of the technology. They use their recommendation systems, which are based on machine learning algorithms, to personalize the content that is suggested to each individual user. Other examples of companies finding solutions that are feasible only because of the AI engine include Match.com, which uses AI-powered matching algorithms to pair users with compatible partners, based on factors such as personality, interests, and preferences. The use of AI in this way allows Match.com to provide a more accurate and personalized matchmaking service than would be possible with traditional methods. OpenAI has developed GPT-3 and ChatGPT, AI-powered language model that can generate human-like text. This technology is used in a wide range of applications, such as chatbots, content generation, and language translation, which would not be possible without the use of AI. Amazon’s Go store uses advanced AI and computer vision technology to create a cashierless shopping experience. The store uses cameras and sensors to track what customers take off the shelves and charge them automatically, making the check-out process faster and more efficient. Google’s DeepMind has developed AlphaGo, a computer program that can play the ancient Chinese game of Go. (b) Innovative solutions generated with the help of AI Some companies are using AI to generate innovative solutions. One example of this is Emotient, which uses AI to analyze facial expressions and body language in order to understand the emotions that people are experiencing. The company’s technology can be used in a variety of settings, including in-store advertising, online video, and social media and allows advertisers to get real-time feedback on how their ads are being received. Realeyes uses AI and computer vision to analyze the facial expressions of viewers to understand how they are responding to an advertisement. Noldus Information Technology uses a combination of AI, computer vision, and behavioral analysis to understand how people are responding to an advertisement.

16 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman In these cases, companies are using AI to generate innovative solutions under alternative scenarios that have the potential to disrupt traditional industries and create new opportunities. 4. AI for value-creation initiatives Companies can use AI for innovative value-creation initiatives, including positioning for target segments, exploring omnichannel distribution and promotion, and developing business and revenue models. One example of a company using AI for innovative initiatives in all aspects of their value-creation offerings is Amazon. The company has implemented AI-powered tools across various areas of the business, such as product recommendations and supply chain optimization, to improve efficiency and drive growth. Amazon uses machine learning algorithms to personalize product recommendations for individual customers, which helps to increase sales and improve customer engagement. Additionally, the company has implemented AI-powered tools in its supply chain operations to optimize inventory management and predict demand for products, allowing for more efficient distribution and promotion. McDonald’s has implemented AI-powered tools to improve the efficiency of its operations and drive growth. For example, McDonald’s uses AIpowered kiosks in its restaurants to improve the ordering process for customers, reducing wait times and increasing sales. Additionally, the company has implemented AI-powered tools in its supply chain operations to optimize inventory management and predict demand for products, allowing for more efficient distribution and promotion. Another example is Amelia AI, which is a conversational AI platform developed by IPSoft (Amelia, 2023). Amelia is widely used by banks to replace customer service employees by providing a 24/7 service for their customers. The platform is trained to understand natural language, answer questions, and complete tasks, such as account balance inquiries, fund transfer, credit card payment, among others. Amelia is also able to integrate with the bank’s existing systems and databases, providing the customer with a seamless experience. With Amelia, banks can improve their customer service, reduce costs, and increase customer engagement and satisfaction. Finally, a company like Tesla uses AI to optimize its manufacturing processes and improve the performance of its electric vehicles. For example, Tesla’s Autopilot feature uses machine learning algorithms to process data from sensors on the vehicle to enable features such as semi-autonomous driving and improve overall vehicle performance. Additionally, Tesla uses AI to optimize the performance of its battery systems, which helps to improve the range and efficiency of its electric vehicles.

Creativity and innovation in the age of AI 17 All these examples show how AI can be used to set objectives for innovative initiatives of the entire value-creation process, thus improving the performance and competitiveness of a company across a range of areas, such as product development, distribution, promotion, and revenue models and in the pursuit of customer engagement and satisfaction and its own short and longterm objectives and vision. 5. AI for the design of an ideal organizational architecture To deliver any strategy, AI can be utilized to align all the elements of the organizational architecture. These include structure, values, processes, business model, performance measures and incentives, required competencies, technology, required resources, supply chain management, and other internal and external resources needed to achieve the organizational vision and objectives. Accenture, for instance, has developed an AI-powered system called “MyWizard” that aids employees in finding information quickly and easily. The system uses natural language processing to comprehend employee requests and provides relevant content from across the company’s internal systems. Another example of a company using AI to design an ideal organizational architecture is GitHub. It has implemented a feature called “Copilot”, which uses AI to match programmers with relevant jobs based on their skills and experience. Another example is IBM, which has implemented an AI-powered platform called “Watson Assistant for HR”, which helps managers with recruitment, employee engagement, and other HRrelated tasks. Salesforce developed an AI-powered platform called Einstein, which helps sales teams with forecasting, lead scoring, and other sales-related tasks. Many companies are using AI to automate and optimize various aspects of their organizational architecture, including HR, operations, and supply chain management. The implementation of AI in these areas can result in improved efficiency, reduced costs, and increased productivity. 6. AI to mobilize and orchestrate a portfolio of innovators Innocentive, one of the pioneers in open innovation, is using AI to connect organizations with a global network of problem solvers. The platform uses AI algorithms to match organizations with the most suitable open talent problem solvers for their specific challenges, based on factors such as skills, experience, and reputation. Another example is Cisco, which historically grew successful acquisitions, and their effective integration is continuing this in the AI area. For example, in 2016 Cisco acquired MindMeld, a start-up that developed an AI-powered

18 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman conversational platform. In 2018, Cisco invested in Perspica, an AI-powered analytics platform for cloud-native applications. They also acquired Accompany, which used AI to create databases of people and companies. The company was acquired to enhance the AI and machine learning capabilities of Cisco’s collaborative portfolio. Another example of a company that uses AI to help construct the portfolio of the needed innovators is Quid, a software company that uses AI to analyze vast amounts of data, such as news articles, patents, and scientific literature, to identify trends and patterns in innovation. They help companies and organizations to identify potential disruptive technologies, emerging trends, and key players in a given field. The AI-powered technology allows Quid to quickly and effectively analyze vast amounts of data, identify patterns and trends, and ultimately help organizations to construct their portfolio of innovators and technologies that align with their strategic goals. In summary, many companies, realizing the importance of AI, are applying a variety of approaches to build the needed competencies in this area including using M&A, building internal talent, accessing open talent, and forming strategic alliances to create an effective portfolio of AI innovators. 7. AI for orchestrating innovation efforts AI can also lead the innovation efforts of a company taking the role of an “enlightened orchestra conductor”. An illustrative example is represented by Google, which has implemented a system called “AutoML”, an AI platform that automates the process of building machine learning models. This system allows non-experts to build sophisticated models with minimal coding, thus empowering team members to perform common goals as part of a coherent orchestra that strives for perfection. AutoML also helps to identify the best models and parameters, reducing the chances of human errors and increasing the speed of model development Google’s AutoML has been used in a variety of industries and applications, resulting in improved performance and efficiency. For example, in the healthcare industry, AutoML has been used to develop models for diagnosing cancer and identifying potential drug therapies. In the financial industry, it has been used to improve fraud detection and credit risk analysis. In term of results, this system has helped customers achieve up to an 18 times faster model development and 37% improvement in model accuracy (Google, 2019) Another example of a company using AI in a similar way is IBM, which has implemented an AI-powered platform called “Watson Studio”, which allows data scientists, developers, and business analysts to collaborate on AI projects. Watson Studio provides a common platform for data preparation, model development, and deployment, helping to align team members toward common goals and to enable them to work more efficiently and effectively.

Creativity and innovation in the age of AI 19 IBM’s Watson Studio has also been used in various industries and applications, resulting in improved performance and efficiency. In the retail industry, Watson Studio has been used to improve customer service and personalization by analyzing customer data and providing personalized recommendations. In the energy industry, it has been used to optimize the performance of wind turbines. IBM has reported that Watson Studio has helped customers to achieve up to 80% reduction in model development time and up to 25% improvement in model accuracy (IBM, 2020) In summary, these companies use AI to lead and orchestrate the innovation process by automating and streamlining various tasks involved in the innovation process, such as building machine learning models, data preparation, and model development and deployment. This helps align team members toward common goals and enables them to work more efficiently and effectively, leading to improved performance and efficiency in various industries and applications. These are valuable coordination tools for the leaders of the innovation initiatives. 8. AI and experimentation for breakthrough innovation A growing number of companies are using AI to facilitate experimentation by automating the analysis of data and coordination of experiments. This leads to more efficient and effective experimentation. Examples of this include the use of AI in drug discovery and development, where companies such as Pfizer and Novartis are using AI to automate the analysis of large amounts of data and to identify new drug candidates. AI algorithms are used to analyze data from various sources, such as electronic medical records, scientific literature, and clinical trials, to identify patterns and potential drug targets. This allows companies to conduct more experiments with fewer resources and in less time. Using AI in the drug development process can reduce the time it takes to bring a drug to market by up to 35% (Accenture, 2022). Another example is in the field of manufacturing, where companies such as GE and Siemens are using AI to automate the analysis of data from manufacturing processes in order to identify patterns and potential inefficiencies. AI algorithms are used to analyze data from various sources, such as sensor data, production logs, and maintenance records, to identify patterns and potential areas for improvement. This allows companies to conduct more experiments with fewer resources and in less time. In the field of marketing, companies such as Adobe and Optimizely use AI to automate the analysis of data from customer interactions and optimize the results of A/B testing and multivariate testing. AI algorithms are used to analyze data. In summary, a growing number of companies are utilizing AI to facilitate experimentation by automating the analysis of data and coordination of

20 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman experiments. This leads to more efficient and effective experimentation, with companies in various industries, such as drug discovery and development, manufacturing, and marketing, using AI to automate the analysis of data and identify patterns and potential areas for improvement. Challenges The implementation of AI presents a range of challenges for individuals, businesses, and societies. One key challenge is the need for businesses to actively engage their human employees in the process of AI implementation to ensure success internally. This may involve reorganizing teams, acquiring new skill sets, and promoting adaptability within the workforce. To effectively manage and prioritize various AI systems and teams for optimal innovation and return on investment, it may also be beneficial for corporations to appoint a corporate AI officer or assure that the CTO is the AI advocate and champion. Another important challenge to consider is the ethical issues surrounding AI, particularly the bias that may be introduced due to the data used for training. Additionally, there are questions surrounding the validity of the results produced by AI systems and whether the benefits of AI justify the hype and promise surrounding the technology. Additionally, there is a need to consider the fit between the AI solution and the users’ needs (as further explored in Chapter 5) and whether most advanced AI systems are geared toward innovators rather than other adoption segments. Finally, there are also important governance and regulatory considerations for AI (as explored in the Appendix), particularly in the United States, the European Union, and other countries. Other challenges may also arise depending on the specific context and implementation of AI.

Conclusions The current chapter has presented examples of the ways in which innovative AI applications have assisted in implementing the eight guidelines for enhancing innovation. The examples illustrate that AI can lead to improved innovation. Specifically, AI has been found to aid in the generation of new ideas, identification of new opportunities, optimization of the innovation process, and enhancement of collaboration and creativity within organizations. It emerges that generative AI is most likely to help enhance creativity and innovation, as it can create new and unique content rather than just analyze existing data. This could be used in a wide range of fields such as natural language processing, design, and art. Most promising are potential opportunities due to Microsoft’s investment in Open AI, that is, the potential impact of the integration of ChatGPT and other Open AI innovations into Microsoft tools, such as Word, Outlook, Bing, and others.

Creativity and innovation in the age of AI 21 Additionally, it is acknowledged that AI plays a fundamental role in many innovative products, services, and experiences that are commonly used. To take advantage of these benefits, managers are encouraged to consider the implementation of AI-powered tools and processes within their organizations. This may include investing in AI-powered ideation tools, utilizing machine learning algorithms to analyze data, or optimizing the innovation process with AI. By embracing AI, managers can enhance creativity and achieve better innovation outcomes. Moreover, AI could strongly impact innovation of our society. In the field of healthcare, AI is being employed to analyze vast amounts of medical data and identify patterns that can lead to the development of new treatments and cures that can result in more personalized and effective treatments for patients (i.e., AI algorithms are being utilized to analyze electronic medical records, genetic data, and imaging data to identify potential drug targets and to improve the accuracy of diagnoses). In agriculture, AI is being used to enhance crop yields and reduce the utilization of resources such as water and fertilizer. For example, AI algorithms are being employed to analyze data from weather sensors, drones, and satellites to predict crop growth and optimize irrigation and fertilization. This can lead to more sustainable and efficient farming practices. In the field of energy, AI is being utilized to optimize the performance of renewable energy systems such as wind and solar power. For instance, AI algorithms are being employed to analyze data from weather forecasts and sensor data to predict energy production and optimize the operation of wind turbines and solar panels. This can lead to more efficient and cost-effective renewable energy systems. In transportation, AI is optimizing the operation of transportation systems such as cars and trains. For example, AI algorithms are being employed to analyze data from sensors and cameras to predict traffic patterns and optimize the routes of cars and trains. This can lead to more efficient transportation systems and reduced congestion and emissions. In the field of smart cities, AI is being used to improve the operation of cities by analyzing data from various sources such as traffic cameras, weather sensors, and social media. For example, AI algorithms are being employed to predict traffic patterns, identify areas of congestion, and optimize the operation of traffic lights. This can lead to more efficient and sustainable cities. Finally for further explanation of the impact of AI on customers and human engagement see the special issue of Management and Business Review on AI for customer engagement (Wind et al., 2023). We hope that this chapter and the following chapters of the book, which explore different uses of AI systems to enhance creativity, inspire you to use AI to help generate ideas, evaluate them, and implement them for the benefit of all.

22 Yoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman Note 1 https://vimeo.com/762420702

References Accenture. Accenture’s innovation architecture uses AI to help companies identify and prioritize innovation opportunities. Retrieved from www.accenture.com/us-en/ insight-innovation-architecture Accenture. Retrieved from www.accenture.com/us-en/services/applied-intelligence/ mywizard-intelligent-automation-platform Accenture. (2022). The Art of AI maturity. Retrieved from www.accenture.com/us-en/ insights/artificial-intelligence/ai-maturity-and-transformation Amazon. Retrieved from www.godatafeed.com/blog/how-amazon-uses-ai-to-dominateecommerce Amazon Go store. Retrieved from www.zdnet.fr/actualites/le-magasin-sans-caissesamazon-go-fait-son-entree-en-europe-39918905.htm Amelia. (2023). Retrieved from https://amelia.ai/fr/; And see Chetan Dube “AI + Human is the Essential Formula for Customer Engagement”, Management and Business Review “AI for Customer Engagement” Cisco. Retrieved from https://techcrunch.com/2017/05/11/cisco-acquires-conversationalai-startup-mindmeld-for-125-million/ Cogito. Retrieved from https://d3.harvard.edu/platform-digit/submission/cogito-ai-for-abetter-human-customer-service-experience/ Domino’s Pizza. Retrieved from https://cloud.google.com/customers/dominos EMERJ. (2023). Artificial intelligence at investment banks: 5 current applications. Retrieved from https://emerj.com/ai-sector-overviews/artificial-intelligence-atinvestment-banks-5-current-applications/ Emotient. Retrieved from www.silicon.fr/avec-emotient-apple-en-saura-plus-sur-vosemotions-135325.html GE. (2023). GE predix platform. Retrieved January 12nd, 2023 from www.ge.com/ digital/predix GitHub. Retrieved from https://dev.to/github/why-use-github-copilot-and-copilot-labspractical-use-cases-for-the-ai-pair-programmer-4hf4 Google. (2019). AutoML: An industry perspective. Google AI Blog. Retrieved from https://cloud.google.com/automl IBM. (2020). IBM Watson Studio: Empowering data scientists to work smarter and faster. IBM. Retrieved from www.ibm.com/downloads/cas/AGKXJX6M Innocentive. Retrieved from https://d3.harvard.edu/platform-digit/submission/innocentiveoutsourcing-innovation-to-a-network-of-solvers/ McDonald’s. Retrieved from https://emerj.com/ai-sector-overviews/artificial-intelligenceat-mcdonalds/ Miller, S. M. (Forthcoming 2023). Singapore public sector AI applications emphasizing public engagement: Six examples. Management and Business Review “AI for Customer Engagement”. Nelson, R. R. (2003, June). Innovation and economic growth. The Journal of Economic Literature, XLI, 789–831. Noldus. Retrieved from www.noldus.com/

Creativity and innovation in the age of AI 23 Quid. Retrieved from https://netbasequid.com/ Salesforce Einstein AI. Retrieved from www.softwareadvice.com/resources/salesforceeinstein-ai-primer/ Starbucks. Retrieved from https://news.microsoft.com/source/features/digital-transformation/starbucks-turns-to-technology-to-brew-up-a-more-personal-connectionwith-its-customers/ Tesco. Retrieved from www.forbes.com/sites/bernardmarr/2016/11/17/big-data-attesco-real-time-analytics-at-the-uk-grocery-retail-giant/?sh=5e51da1161cf Tesla. Retrieved from https://bernardmarr.com/how-tesla-is-using-artificial-intelligenceto-create-the-autonomous-cars-of-the-future/ Wind, J., & Crook. (2006). The power of impossible thinking: Transform the business of your life and the life of your business. Pearson FT Press Upper Saddle River, NJ. Wind, J., Pagani, M., & Dischler, J., (Eds). (Forthcoming 2023). Management and Business Review “AI for Customer Engagement”

2

Could Artificial Intelligence make us humans more creative? Margherita Pagani and Renaud Champion

Introduction In today’s digital economy, Artificial Intelligence (AI) systems have already emerged as powerful methods to help humans solve increasingly complex business problems, but there is still a perception that AI systems and automation may hamper human creativity. While we’ve made great progress in leveraging AI systems to aid in the creative process, machines ultimately can’t think or feel and need coaching from humans. However, the possibility to rely on AI and machine learning (ML) to automate workflows can take an organization to new and bold places, allowing it to focus its time and resources on more creative tasks and stimulate a different way to think. But could AI systems be a catalyst to push our human creativity? Creativity is defined in the literature as the ability to produce novel and useful ideas and one of the most striking capacities of humans which we display in our thoughts and behaviors (Amabile & Pratt, 2016). This capacity is relevant for consumers and businesses (Mehta & Zhu, 2016). Consumers engage in creative behaviors to satisfy their needs and solve consumption-related problems (Burroughs & Mick, 2004), gain enjoyment (Dahl & Moreau, 2007), and relax (e.g., through the use of coloring books; Harrison, 2016). Creative employees enjoy challenges requiring an innovative approach, bring fresh concepts to the table, are not afraid to challenge the way things are done, and see a better way to make the world work. That’s why companies need human skills such as creativity (Mehta et al., 2017; Pagani & Champion, 2021) even in an age of automation, machine learning, and Artificial Intelligence (AI). Various factors were proven to influence creative ability and performance, including external constraints (Moreau & Dahl, 2005), involvement (Burroughs & Mick, 2004), analogical thinking (Dahl & Moreau, 2002), systematic training, and life experiences (Maddux & Galinsky, 2009). Recommendation systems use AI and machine learning to comb through real-time, contextually relevant data about customers’ characteristics and interactions to suggest a smaller, more manageable set of “likely” options that can be used by marketers for more personalized customer interaction. AI systems DOI: 10.4324/9781003287582-4

Could Artificial Intelligence make us humans more creative? 25 also allow the collection of real-time, AI-screened relevant data to diagnose customer sentiments, stimulating new solutions and offers. AI-powered, conversational chatbots, managing the more routine tasks, allow customer service agents to spend more time dealing with the exceptional customer interactions that they encounter in which creativity and empathy will play a role. In the arts field, AI systems can already execute complex and creative tasks such as writing poems, mimicking the style of great painters, or making creative decisions in filmmaking. AI systems advancements have begun to transform and aid creative processes as well as challenge what we believe to be creative thinking, and there is no doubt that the boundaries of AI’s role in creative endeavors will be pushed further. Despite all of this, even if AI systems can certainly offer many benefits by serving as smart, efficient, and inspirational assistants, research has not fully investigated how AI systems may inspire the human soul of creativity. Consequently, we know surprisingly little about the real influence of AI systems on individual-level creativity and how this may be used as a collaborative tool to spur it. In this chapter, we address this unresolved issue and specifically consider how Artificial Intelligence systems may foster individual creativity, defined as “the production of novel, appropriate ideas in any realm of human activity, from science to the arts, to education, to business, to everyday life”, thus the ideas have to be new and appropriate to the opportunity or problem presented (Amabile, 1996). First, we introduce computational creativity and human creativity. Then we describe three different levels of observed creativity of the AI systems. Finally, we specifically consider creative industries and present some findings emerging from a study conducted on how artists are using Artificial Intelligence systems in their creative endeavors to investigate their experience more in-depth. We identify three main ways AI can influence the creative process and provide guidelines for managers (see Figure 2.1).

Introduction to human creativity and computational creativity Levels of observed creativity of the AI systems How artists are using AI Systems in their creative endeavors Three main ways AI can influence the creative process

Figure 2.1 Structure of the chapter

26 Margherita Pagani and Renaud Champion Managers as artists need visions and passion to achieve their goals and imagination and audacity to redesign their organizations. Moreover, implementing a new management strategy into action implies taking decisions never tried before and questioning the way a company has always done things. Our research question in this chapter will be: could Artificial Intelligence make us humans more creative?

How creativity is generated in the human brain Creativity is the ability to produce original and valuable ideas or behaviors (Shi et al., 2017), to find hidden patterns, to make connections between seemingly unrelated phenomena, and generate solutions. Moreover, creativity needs to be nurtured and fostered to develop and flourish. Although the observed AI systems are starting to be applied to creative tasks, it is important to remind that they are created by human intelligence and represent only a human endeavor. Neuroscientists and psychologists, (Kleibeuker et al., 2013; Bressler & Menon, 2010) in their attempts to make a connection between creative thought processes and the parts of the brain that may process them, have defined creativity as requiring the mixing and remixing of mental representations to create novel ideas and ways of thinking. They also found that creativity involves two main processes: thinking, then producing. Since creativity is among one of the most complex human behaviors, it likely requires the coordination of multiple brain regions and types of thinking. Neuroscientists and psychologists (Kleibeuker et al., 2013) have identified three specific brain networks in charge of the creative thinking process (Bressler & Menon, 2010): • The Executive Attention Network is about targeted attention and focus, and it is linked to the brain areas associated with decision-making, complex behavior, and spatial sensory information. • The Imagination Network is about brainstorming, daydreaming, and social cognition and is linked to brain areas associated mainly with mental simulation, imagination, and spontaneity. • The Salience Attention Network works as a switch button between the two previous networks and manages the interactions between them. The creativity level depends on the speed of these interactions. The Imagination Network is in charge of generating ideas with a spontaneous combination of memory retrieval and mental simulation (parallel with AI: simulating brand new concepts using historical data). Then the Salience Attention Network selects idea candidates and switches to the Executive Attention Network, which is in charge of the higher-order processing, idea evaluation, elaboration, and revision (formal decision-making to achieve optimization)

Could Artificial Intelligence make us humans more creative? 27 Because creativity is so complex, it seems naive to think that creativity can be localized to a single region in the brain. For a long time, scientists (Kleibeuker et al., 2013) found that the right hemisphere (side) of the brain called the anterior cingulate cortex (ACC) was associated with creativity. However, studies (Fink et al., 2009; Mashal et al., 2007) that looked at the activity in the brain while people were doing creative tasks or in patients who had brain damage that resulted in difficulty with creativity showed that other parts of the brain are also involved in creating, such as the left inferior parietal lobule (IPL) (Fink et al., 2009), right angular gyrus (Fink et al., 2009), the dorsolateral prefrontal cortex (DLPFC) (Kleibeuker et al., 2013) and left middle temporal gyrus (MTG) (Mashal et al., 2007) or all the three networks (Beaty et al., 2015). Also, they suggested that the distributed network included a few significant clusters that belong to the temporal lobes (e.g., the MTG bilaterally), regions that are associated with semantic and episodic memory retrieval. Even if, based on the findings of these prior studies, there is no clear consensus about the neural basis of creativity (Shi et al., 2017), there is evidence that creative processes involve cycling between generation and exploration, with the pre-inventive form altered and updated with each cycle until a satisfactory outcome is achieved (Moreau & Dahl, 2005). The ability of Artificial Intelligence to process a huge amount of data in real time helps humans in this cycling between generation and exploration. AI systems (i.e. Generative Pretrained Transfromers models such as GPT3 or chatGPT) can stimulate idea generation, and machine learning systems allow the exploration.

Computational creativity When a computational agent (or software) tries to model, simulate, or replicate human creativity, we talk about computational (or artificial) creativity. Computational creativity is a multidisciplinary endeavor at the intersection of the fields of Artificial Intelligence, cognitive psychology, philosophy, and the arts. It is also defined as a branch of Artificial Intelligence and the study of building software that exhibits behavior that would be deemed creative in humans, including the production of visual and moving image art (Avila & Bailey, 2016), literature, and music (Cope, 1989) but also more broadly in areas such as games (Chen, 2016). Computational creativity is designed to nurture and enhance the user’s creativity and creative practice and considers the production of software that acts as a creative collaborator rather than a mere tool. Several scholars (Kontosalo & Jordanous, 2020; Davis, 2017; Colton & Wiggins 2012; Hoffmann, 2005) provided categorization and identified the aspects and levels of the computer’s input on creativity. According to Hoffman (2005), computational creativity may collaborate with the human allowing them to model or explore the design space to boost the novelty and divergent aspect of creativity. It could also help the evaluation when computers analyze products for their originality and/or value. Davis (2017) features three categories: (a)

28 Margherita Pagani and Renaud Champion “creative support tools” (i.e., CAD) that can support or boost the person’s abilities, (b) “generative agents” or algorithms able to generate products on their own, (c) “colleagues” that interact and communicate as well. Boden (2009) characterizes creative behaviors in terms of computer exploration, whether combinatorial or transformational. Even if Artificial Intelligence systems, or machines performing cognitive functions that are usually associated with human minds, such as learning, interacting, and problem-solving (Raisch & Krakowski, 2021), can already accomplish creative and complex tasks (Pagani & Champion, 2020), only recently have management scholars started considering the role of AI systems on the creative process in management. Verganti et al. (2020) showed how AI systems shift the focus of human creativity by enabling individuals to spend more time choosing the problem they wish to solve. AI systems were found to provide stimuli to foster novel and useful connections allowing the increase in quantity and novelty of the ideas generated (Chen et al., 2019; Mikalef & Gupta, 2021). Other studies show a positive effect on design creativity, providing suggestions that allow designers to come up with refined ideas by focusing on professional judgment and aesthetic sensibility (Wilson & Daugherty, 2018). AI systems can also contribute during “brainstorming” sessions by underlining overlooked features of a problem to reduce fixation through the stimuli (McCaffrey, 2018) or find very technical solutions to problems (Chalmers et al., 2021). This category of AI systems is called Generative Pre-trained Transformer (GPT-3 or ChatGPT) and includes autoregressive language models that use deep learning to produce human-like text. Given an initial text as a prompt, it will produce text that continues the prompt. Overall, AI systems automate manual tasks, freeing up time and resources to be creative (Mikalef & Gupta, 2021). When we consider the art industry, the role of computational creativity applied to arts (AI art) is no longer limited to the digitization of artworks and cost-effective enablers of pixelized artworks. There are many mechanisms for creating AI art, including procedural “rule-based” generation of images using mathematical patterns, algorithms, and artificial intelligence or deep learning algorithms such as generative adversarial networks and transformers. Nowadays, computers can actively participate in the creation process (collaborating with the creators or helping managers with more creative tasks), but a void of research emerges on the effects of computational creativity on human creativity and how AI may foster it.

Different levels of creativity in AI systems applied to creative tasks In the cases described previously, computational creativity software can be used not only for autonomous creative tasks (writing poems or painting pictures) but also to act as a creative collaborator rather than a mere tool. For this reason, we try now to understand how AI systems may be used in creative tasks.

Could Artificial Intelligence make us humans more creative? 29 We conducted a study on a dataset of more than 800 AI systems in action in 14 industries and identified a sample of 84 cases of AI systems applied to tasks requiring high creative abilities (such as design, writing, and composing music) as opposed to routine and automated tasks not requiring creative skills (Pagani & Champion, 2020). Based on their ability to produce ideas that are novel and useful, we classified the AI systems according to three main levels of observed creative characteristics. Mimicking human cognition A first cluster is represented by AI systems that allow getting predictions by going through relevant data. In this case, algorithms are trained to recognize patterns and make probabilistic decisions. This is the case of AI systems applied, for instance, to choreography where, for each dancer, it is possible to get iterative versions of a specific idea. For example, a choreographer may prompt: “I’m starting with this phrase, and I’d like the AI to invent the next phrase – but in the style of Jordan, or the style of Jess. And then you can get combinations of those”. It’s learning all the time and feeding back, so this iterative version gives you all of these new possibilities you couldn’t have imagined. It is worth mentioning also the experiment Living Archive realized by the choreographer Wayne McGregor and Google Arts & Culture Lab, a tool for choreography powered by machine learning. The tool generates an original movement inspired by Wayne McGregor’s 25-year archive, creating a live dialogue between dancers and his body of work (Figures 2.2 and 2.3). It is also the case of videos and pictures of non-existing people generated by existing data or of mimicking famous artists and spotting forged artworks.

Figure 2.2 Choreography powered through AI tool | Google Arts & Culture Source: Living Archive: Google Arts & Culture/Studio Wayne McGregor 2022 www.youtube.com/ watch?v=qshkvUOc35A

30 Margherita Pagani and Renaud Champion The neural network is capable of mimicking the “style” of an image and using that style to copy another image. Synthesizing high-quality human faces, Pantheon Lab has developed a Face Synthesis technology that can create custom virtual agents for image and video synthesis (Figure 2.4).

Figure 2.3 An example of the AI tool predicting a dancer’s next move Source: Living Archive: Google Arts & Culture/Studio Wayne McGregor 2022

Figure 2.4 Digital Humans generated by AI Source: Digital Humans generated by Pantheon Lab Limited www.pantheonlab.ai/technologies

Could Artificial Intelligence make us humans more creative? 31 Table 2.1 Illustrative cases of AI systems mimicking human cognition • •

Ai-Da uses a robotic arm system and facial recognition technology paired with artificial intelligence to create art. The system can analyze an image put in front of the machine, which feeds into an algorithm to produce the robot’s arm movements. Similarly, the musician Reeps One began an experiment training a machine to emulate his voice. The deep learning AI system was able not only to replicate his musical composition but also to predict what would follow.

Creativity is inspired thanks to deep learning technologies that humanize machines visually, virtually, and intellectually. It is also the case of intelligent autonomous systems (self-driving cars) that can flexibly and rationally respond to stimuli and environmental situations that they have not met before or which have not been programmed in advance. Thus, the stimulus independence typical to the human mind or streams of thoughts and images unrelated to immediate sensory inputs (Teasdale et al., 1995) should be one of the main criteria of (human-like) intelligence of autonomous systems. Some illustrative cases are indicated in Table 2.1.

Combining concepts A second cluster includes AI systems able to combine different concepts such as styles of music, melodies, or pictures, generating new alternatives. Through machine learning, the AI system can learn from huge amounts of data. It can then mix different sets of data to come up with pieces of artwork that can then stimulate human creativity. Instead of coming up with one creative idea, artists can compare a lot of different outputs and go from there, stimulating their creative process. Generative AI systems such as DALL•E 2 or Midjourney are able to learn the relationship between images and the text used to describe them. They use a process called “diffusion”, which starts with a pattern of random dots and gradually alters that pattern toward an image when it recognizes specific aspects of that image. This AI system is also able to generate more realistic and accurate images with great resolution (Figure 2.5) This is also true in the context of intelligent factory automation, where the combination of AI-augmented management software with advanced sensors, autonomous ground vehicles (AGV), or robots can generate creative ways of optimizing the flows in a warehouse or running a production line. Table 2.2 shows some illustrative cases of AI systems able to combine concepts.

Ideating to build novelty AI systems may also compose music, create works of art and sculptures, design objects, and write songs or poems. LyricJam, for instance, is an AI

32 Margherita Pagani and Renaud Champion

Figure 2.5 Examples of paintings generated by AI Source: Pictures generated with DALL•E 2 and DreamStudioAI provided by IBM

Table 2.2 Illustrative cases of AI systems combining concepts •

• •

MuseNet is a deep neural network that can generate four-minute musical compositions with ten different instruments and can combine styles. This AI system discovers patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. GPT-4 can generate convincing text and images in various styles for short stories, songs, press releases, and technical manuals. ABB Genesis project brings to reality the full automation of semiconductors’ complex manufacturing process. ABB robots were programmed to work in collaboration with mini AGVs and automated installations under the control of a single operator through an innovative manufacturing operations management system.

Could Artificial Intelligence make us humans more creative? 33 Table 2.3 Illustrative AI systems for novelty generation •

•

•

Huawei uses AI in its Huawei Mate 20 Pro smartphone to create new melodies. Engineers fed music, in the form of data, into the phone’s dual neural processing unit – so the AI had information about the timbre, pitch, and meter that, for example, Schubert liked to use in his melodies. The AI then created melodies from that information, and the composer chose his favorites, orchestrated those melodies, and turned them into final two movements to complete Schubert’s “Unfinished” Symphony. Aiva (Artificial Intelligence Virtual Artist) was trained by its creators to compose classical music. Through an analysis of a vast collection of music partitions written by renowned composers (such as Mozart, Beethoven, and Bach), it is able to construct a mathematical model representing the essence of music. Using this model, Aiva generates new music and multiple soundtracks. Medical Micro Instrument, by making surgical instrumentation smaller, adding wrist-like mobility, and manipulating it with a robot, can reduce tremors and scale surgeons’ hand movements to allow procedures that seemed unimaginable in the fields of pediatric microsurgery or lymphatic channels reconstruction.

system created by researchers at the University of Waterloo’s Natural Language Processing Lab that listens to live music and generates lyrics in real time to match the song being played (Facebook IQ, 2022). The software uses a neural network to analyze chord progressions, tempo, and instrumentation and then suggests words that reflect the mood of the melody. This inspires the artist with new ideas and expressions. These experiments in computational creativity are enabled by the dramatic advances in deep learning that makes these AI systems flexible and able to learn in an unsupervised manner to take on a wide variety of tasks. These AI systems are also able to discover patterns and generalize from them (accidental creativity). What’s more, the inherent element of randomness within deep-learning algorithms leads to variability in the models’ output lending to creative applications. The human examines the outputs and teaches the AI system how to select from the many combinations. Similarly, surgical robots and AI-augmented diagnosis systems can enable practitioners to design new innovative procedures to address previously impossible surgery or identify earlier undetectable tumors. Some illustrative cases are indicated in Table 2.3.

How AI systems impact creative practice: a study on artists AI systems applied to creative or complex tasks may not only be used as a workman-like tool. The creative characteristics we have identified may also foster human creativity inspiring the different stages of the human creative

34 Margherita Pagani and Renaud Champion thinking process. To confront these findings we addressed professionals of creativity using the previously mentioned AI tools. We refer in this section to a study in progress based on in-depth interviews with artist pioneers in Artificial Intelligence art (AIArtists.org) to learn how they use different AI systems classified in the three main categories described before and how AI impacted their creative practice. The study focuses on artists, given the creative characteristics and innovative ability of their task that has been found similar to the activity of the entrepreneur and designer (Chaston & Sadler-Smith, 2012). Artists, like entrepreneurs and managers, have a product or idea to sell. They decide to start their businesses because entrepreneurship can embody their interests, skills, and talents in their work (Paige & Littrell, 2002). Based on the interviews conducted with a large sample of artists using AI in their activity (AI Artists), three main dimensions of impact have been observed: 1. AI system as a new instrumental resource Some artists mainly consider Artificial Intelligence, machine learning, and generative code as instrumental new resource in their latest work. Like any other new tool, the depth of AI’s creative potential has only begun to be even scratched. For me, in my work, machine learning is a tool, just like ink or charcoal. It does different things and offers a different history when you choose to work with it. Its importance will only increase. To ignore it just seems to be willful. (artist 1) 2. AI system as a tool to explore possibilities Artificial Intelligence systems were also found to inspire imagination and foster new ideas inspiring human creativity and allowing to explore possibilities. I think it is expanding human creativity to new levels. You have to have the imagination to have creativity, but the human imagination is limited because we are constrained by our world. The machine is good at exploring possibilities, so if we can frame the machine to explore possibilities for you in the art space, then the machine can give you lots of new ideas. (artist 2) “Running through all my practice is my relationship with technology. I’m fascinated by how AI might explore the potential of choreography. Normally I ask my dancers to make iterative versions of an idea. This does 400,000 iterations. The canvas is way bigger. (artist 4)

Could Artificial Intelligence make us humans more creative? 35 3. AI system as true inspirator and teacher AI can also drive inspiration that will have effects on how people develop and experience visual expression, disseminate information, and generate advertising campaigns. The rewards of creative expression are boundless. Some of the artists interviewed describe the AI systems as inspirators. My AI has been a virtuosic collaborator. And, as anyone who has had the good fortune to work closely with a virtuoso knows, my AI partner has been a true inspiration and teacher. I literally see the world differently now; my extensive exposure to DeepDream’s way of interpreting my landscape images has caused me to see actual landscapes differently at times, especially in certain lighting conditions. It has enhanced my ability to see creatively. (artist 5) But if they give us new ways to experience and understand the world around us, then they’re enabling new ways for us to imagine our future. (artist 6) It’s mostly acted as a new medium to explore both on the technological side (“What else can this do?”) and on the artistic side (“How do I control or guide this thing, to express myself”). The impact ML is and will be having in a wider societal sense has also given me much to think about and is influencing how and what I’m trying to express artistically. (artist 7) 4. AI system as a tool to deconstruct the creative process Other artists described their use of AI systems as a symbiotic process to better understand the creative process itself and deconstruct it in logical steps. What I’ve discovered, through the process of helping the machine learn nature, is that it is indeed a symbiotic process. The “artist” must tune the imagery that’s put into the “machine” to craft its interpretation of nature. And the artist must continue to select the work that the machine creates (much like photographers would use a contact sheet) to make the most unique, and frankly beautiful, interpretation of nature. (artist 8) Being able to engage with mark-making in collaboration with a robot means not always knowing what I’m doing – and that has been enlightening. It’s helped me work through and question what narratives we tell when we engage in collaboration with mechanical agents, and technologies in general. In the conversation of AI, that gets broad – dystopian, utopian, occasionally fraught with controversy. When people think about AI

36 Margherita Pagani and Renaud Champion there is a tendency to ascribe or imagine, considerable agency. Something like an artificial consciousness, however far-reaching that might be. I’m compelled by the human capacity to anthropomorphize our relationship with machines, particularly with robots, and how that can end up being a mirror for how we view ourselves and our interactions with others. Some didactic models are encouraged by developments in IoT and voice interfaces. But the collaborative models are more interesting to me. It’s a new stage for examining authorship and agency. It starts to question, who is in control? Whom do we want to be in control of? Is that the point? (artist 9) Some artists reported also that attempting to teach machines how to paint has forced them to reverse engineer their creative process and, as a result, come to a more profound understanding of the creative process With each painting my robots create, I am attempting to teach them how to independently make all the aesthetic decisions that I make when I paint. This has been a fascinating process because, in order to teach my creativity to a machine, I am forced to deconstruct the process into logical steps. Sometimes I am successful in my attempts, and other times I fail. But when I do find an algorithm that performs a specific artistic function, and it performs that function well, I can not help but wonder if that is what is happening in my own mind. (artist 10) This process of deconstructing creativity gives artists insights into how their creativity works – and a clearer understanding of how their mind works. This allows a deeper understanding of themselves by becoming more familiar with their creative process Table 2.4 classifies the tools used by each artist according to the three categories and shows the impact on the creative experience highlighted by the artists. Table 2.4 How has AI impacted creative practice? – selected interviews MIMICKING HUMAN COGNITION Artist (Choreographer)

Artist (Painter)

The impact ML has and will be having in a wider societal sense has also given me much to think about and is influencing how and what I’m trying to express artistically. Attempting to teach my machines how to paint has forced me to reverse engineer my own creative process, and as a result come to a more profound understanding of it. . . . I am forced to deconstruct the process into logical steps. (Continued)

Could Artificial Intelligence make us humans more creative? 37 Table 2.4 (Continued) Artist working on projects in public areas Artist working with selfgenerated data sets and the creative potential of machine learning Artist and roboticist who explores humanity through art and technology COMBINING CONCEPTS Artist and technologist

Artist, programmer, and leading educator in the field of creative AI

Once you bring in machine learning algorithms, AI algorithms, then you suddenly have a cognitive capacity in your hand as an artist. Machine learning is a tool, just like ink or charcoal. It does different things and offers a different history when you choose to work with it. AI, machine learning, and generative code have all been instrumental in my latest work.

Through this work I have come to realize that AI is a new tool that gives us new ways to experience and understand the world around us, enabling new ways for us to imagine our future. I am interested in generative models trained on large crowd-sourced datasets to explore the idea of collective imagination, as well as uses of realtime, on-the-fly learning for creating interactivity, especially in the context of live performance.

IDEATING TO BUILD NOVELTY Founding creators of the AI AI contributed a level of intricacy, mystery, art movement and grace to my work that would have been prohibitively difficult if not impossible for me to attain by my own hand and imagination. In that sense, my AI has been a virtuosic collaborator, a true inspiration, and teacher. It has enhanced my ability to see creatively. Award-winning artist I’m compelled by the human capacity to who uses hand and anthropomorphize our relationship to machines, technologically reproduced particularly to robots and how that can end up marks to explore being a mirror for how we view ourselves and communication between our own interactions with others. people and machines Professor, researcher, and I think it is expanding human creativity to totally entrepreneur whose new levels. You have to have imagination to have pioneering work explores creativity, but the human imagination is limited whether AI can be because we are constrained by our world. The creative without human machine is good at exploring possibilities, so if we intervention can frame the machine to explore possibilities for you in the art space, then the machine can give you lots of new ideas.

How AI can inspire individual creativity When a business develops or implements tools to help people overcome creative challenges it is not just about making AI better in isolation but making it better in a way that complements humans so that you can have more effective human-AI teams working toward common goals. We thus derive three main ways in which AI can inspire individual creativity.

38 Margherita Pagani and Renaud Champion 1.

Inspire agile methods

Agile methods help organizations move from rigid to resilient, transforming how they get work done. Companies that have scaled AI across the business and achieved meaningful value from their investments were found to dedicate 10% of their AI investment to algorithms, 20% to technologies, and 70% to embedding AI into business processes and agile ways of working (BCG, 2022). In other words, these organizations invest twice as much in people and processes as they do in technologies. AI can help design new agile approaches, especially for innovation and project management. For instance, AI systems mimicking human cognition can be used as a tool • to help the decision-making in complex environments and suggest optimized solutions to assist in the creative process (i.e., the case of Starck– Kartell and Autodesk using AI systems to solve design manufacturing challenges described in Box 2.1). • to reverse engineer the creative process and come to a more profound understanding of it (i.e., the case of attempting to teach the machine how to paint). • to complete repetitive and deterministic tasks so that practitioners can focus on more creative ones (i.e., with filmmaking, 99% of the work is going through hundreds of hours of video in some cases to arrive at the core pieces to use. So there’s still a very good reason to use technology as an assistant here rather than replace the human in the loop).

Box 2.1 The case of Starck-Kartell-Autodesk Starck-Kartell-Autodesk: AI and design Phillipe Stark has teamed up with Kartell, a company specializing in quality plastic furniture design and manufacture, and Autodesk, one of the world’s most prominent 3D software and engineering companies, to solve a puzzling furniture design challenge. The company applied AI to design a chair that would comfortably sit a person while demanding the least possible amount of materials in production. Keeping the standards of both Starck and Kartell, not only should the chair be aesthetically pleasing with a minimal sleek profile, but it should also satisfy all structural requirements that will pass any certification needed, which prompted the use of Autodesk’s prototype software.

Could Artificial Intelligence make us humans more creative? 39 The team was involved in developing the algorithm to process their instructions and concepts. The research has resulted in the most creative outcome achieved through generative design. The chair, “A. I”. was launched at the International Furniture and Design Week 2019 in Milan’s Salone del Mobile exhibit. The chair was built as the first production chair in the world created through the use of Artificial Intelligence. The chair was created through a combination of Artificial Intelligence and human participation, where human involvement was limited to assisting the AI during the production process. This collaboration enhances a designer’s capacity for productivity and innovation.

2.

Inspire out-of-the-box thinking

A second way in which AI can boost creativity is by inspiring out-of-the-box thinking. This is the “first level” of creativity. AI systems combining heterogeneous datasets may suggest novel ideas to boost human creativity. It is neither the mimic world nor the pure ideation, but AI can be used as a switch button or a balance to mix pure imagination with optimization leveraging unexpected and various sources of information. • AI is an “enlightened” (thanks to data and combination) collaborator of the human creative mind. • AI, through generative models trained in large crowd-sourced datasets, can explore ideas that influence managers, artists, and curators to create services and work which society is more likely to engage with (this is the case of recommendations algorithms, such as Netflix and Spotify). • AI stimulates cross-fertilization by confronting managers with possibilities out of their primary expertise (as in the case of intelligent factory automation). 3. Inspire imagination When AI systems generate new ideas, allowing humans to better understand themselves and open new creative routes (imagination), they may challenge

40 Margherita Pagani and Renaud Champion humans and lead toward innovation. We distinguish different ways in which AI can inspire imagination: • AI systems can generate purely novel ideas based on innovative analysis of datasets (AI has a holistic approach to creativity thanks to the numerous sources of information it can play with). • AI systems can be at the origin of novel creations without human intervention (such as the use of robotics in the hospital of the future to deal with logistics and transport or enable new procedures in the OR). • AI as a virtuosic collaborator and source of inspiration (such as the use of AI to train experts through the simulation of unseen and extreme situations or to inspire professionals in the domain of construction design, industrial design, and artistic design). Figure 2.6 summarizes the three types of observed creative characteristics in AI systems and how they may foster individual creativity.

Conclusions

AI SYSTEMS

Findings from this chapter show that the AI systems may have different levels of observed creativity and may be used as collaborators (mimicking the human), inspirators, or to ideate a new concept or solution. The experiences associated with the interaction with AI systems may range from a pure collaborator, inspirator, or help for a deconstruction of the creative process. Even

MIMICKING HUMAN COGNITION

INSPIRE AGILE METHODS

COMBINING CONCEPTS

INSPIRE OUT OF THE BOX THINKING

IDEATING TO BUILD NOVELTY

INSPIRE IMAGINATION

Figure 2.6 Understanding the three types of creative characteristics of AI systems Source: Pagani and Champion (2021) Harvard Business Review France

Could Artificial Intelligence make us humans more creative? 41 if AI systems can show different levels of creativity and be able even to realize an art craft, the real creativity belongs to the human who creates the prompt and controls it telling the machine what to do. Overall, AI systems may help to enhance individual creativity, and this interaction opens a void of research on how managers can effectively use AI not only to inspire agile methods (rapid and more accurate decisions) but also to augment their ability to think outside the box and inspire imagination.

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42 Margherita Pagani and Renaud Champion Davis, N. M. (2017). Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation. Ph.D. Thesis, Georgia Institute of Technology, Atlanta, GA, USA. Facebook IQ. (2022). How AI will elevate the future of creativity. Retrieved September 3, 2022 from www.facebook.com/business/news/insights/how-ai-will-elevate-the-futureof-creativity Fink, A., Grabner R. H., Benedek M., Reishofer G., Hauswirth V., Fally M., Neuper C., Ebner F., & Neubauer A. C. (2009). The creative brain: Investigation of brain activity during creative problem solving by means of EEG and fMRI. Hum. Brain Mapp., 30, 734–748. Harrison, S. (2016). Fueling, curating, connecting and fascinating: Why and how creativity provokes curiosity. In M. Skerlavaj, M. Cerne, A. Dysvik, & A. Carlsen (Eds.), Capitalizing on creativity at work: Fostering the implementation of creative ideas in organizations (pp. 76–85). MA: Edward Elgar Publishing, Inc. Cheltenham, UK. Hoffmann, O. (2005, December 10–14). On understanding human-computer co-creativity. In Proceedings of the Computational and Cognitive Models of Creative Design VI: Reprints of International Conference of Computational and Cognitive Models of Creative Design VI, Heron Island, Australia; Gero, J. S., Maher, M. L., (Eds.); Key Centre of Design Computing & Cognition: Sydney, Australia. Kleibeuker, S. W., De Dreu, C. K., & Crone, E. A. (2013). The development of creative cognition across adolescence: Distinct trajectories for insight and divergent thinking. Dev. Sci., 16, 2–12. Kontosalo, A., & Jordanous, A. (2020, April 6–9). Role-based perceptions of computer participants in human-computer co-creativity. In Proceedings of the 7th Computational Creativity Symposium AISB, London, UK. Maddux, W. W., & Galinsky, A. D. (2009). Cultural borders and mental barriers: The relationship between living abroad and creativity. Journal of Personality and Social Psychology, 96(5), 1047–1061. Mashal, N., Faust, M., Hendler, T., & Jung-Beeman, M. (2007). An fMRI investigation of the neural correlates underlying the processing of novel metaphoric expressions. Brain Lang., 100, 115–126. McCaffrey, T. (2018). Human-computer synergy in creative problem solving. Proceedings of ISPIM Conferences (pp. 1–10). Stockholm. Mehta, R., & Zhu, M. (2016). Creating when you have less: The impact of resource scarcity on product use creativity. Journal of Consumer Research, 42(5), 767–782. Mehta, R., Dahl, D. W., & Zhu, R. J. (2017). Social-recognition versus financial incentives? Exploring the effects of creativity-contingent external rewards on creative performance. Journal of Consumer Research, 44(3), 536–553. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. Moreau, P., & Dahl, D. W. (2005). Designing the solution: The impact of constraints in consumer creativity. Journal of Consumer Research, 32, 13–22. Pagani, M., & Champion, R. (2020). Making sense of the AI landscape. Retrieved from HBR.org. Pagani, M., & Champion, R. (2021). Comment l’IA peut booster la créativité de votre entreprise .Harvard Business Review France. Retrieved fromwww.hbrfrance.fr/chroniquesexperts/2021/05/35617-comment-lia-peut-booster-la-creativite-de-votre-entreprise/

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Creative AI platforms AIVA The artificial intelligence composing emotional soundtrack music. Retrieved from www.aiva.ai GPT-3. Retrieved from https://openai.com How to compose with MuseNet a piece of music in the style of Mozart. Retrieved from https://ars.electronica.art/aeblog/en/2020/01/17/musenet/

Part Two

From individual creativity toward business creativity

3

When Artificial Intelligence systems help to inspire creative new venture ideas Nathan Sorin and Margherita Pagani

Introduction Supercomputing technologies are enabling new types of product development and innovations that leverage the power of computing and simulation methods, on the one hand, and the potential of data analytics and AI, on the other. For example, pharmaceutical firms such as Merck are investing in ever more advanced technologies such as quantum machine learning in drug discovery. The application of emerging technologies is not limited to the identification of solutions to well-identified, scientific problems. All stages involved in bringing an innovative solution to the market are impacted. There are now firms that extract, qualify, and harness worldwide knowledge with AI to find problems (detecting market needs) and validate solutions to these problems by identifying what markets are most receptive to the solutions. Consequently, algorithms are enabling entrepreneurs to grasp more thoroughly what people want. This ability is fundamental for entrepreneurs, and the design thinking approach relies on it (Brown, 2008). Between problem-finding and solution validation, entrepreneurs must generate ideas regarding novel and appropriate solutions to problems in the form of new products or services (Perry-Smith & Coff, 2011) that can be the basis for a new venture if implemented. To generate a new venture idea, entrepreneurs are exposed to information on uncovered needs that they process (Vaghely & Julien, 2010). For example, the idea of the carpooling platform BlaBlaCar came up when its founder, Frédéric Mazzella, planned to celebrate Christmas with his family and realized that unfortunately there were not any train seats available. He asked his sister to drive him to the family house and observed during the trip that most cars were empty (information exposure). He noted there was an opportunity to tap into by connecting drivers with travelers through the development of a digital platform (information processing). The nature of this task may intuitively seem automatable, as AI systems process information as well, but this task requires creativity (Perry-Smith & Coff, 2011; Ward, 2004). There is a growing sentiment that some AI systems can help humans complete creative tasks rather than replace humans (Amabile, 2020; Townsend & Hunt, 2019). DOI: 10.4324/9781003287582-6

48 Nathan Sorin and Margherita Pagani Therefore, entrepreneurs and organizations that want to explore new growth paths (March, 1991) should consider AI systems as facilitators. We define generative AI systems as a group of AI systems that use generative modeling and deep learning to produce synthetic content (e.g., text, image, 3D object, music) at scale (Jovanovic & Campbell, 2022). AI techniques used to build generative AI systems include Generative Pre-Trained Transformer models (e.g., ChatGPT, GPT-3, LaMDA), Generative Adversarial Networks (e.g., 3D-GAN), and Generative Diffusion Models (e.g., DALL•E 2). In this chapter, we specifically explore how generative AI systems used as input to generate ideas can influence the average level of creativity displayed by entrepreneurs and the number of creative ideas they may generate, depending on whether cognitive flexibility is stimulated or not. The current chapter is structured as follows. First, we define the main concepts related to entrepreneurial creativity. We then cover some use cases of generative AI systems before providing evidence on the positive effect of generative AI systems in stimulating new venture ideas. Finally, we discuss the theoretical and practical implications of our findings and our contribution.

The entrepreneurial creativity process Entrepreneurial creativity is a special case of creativity, defined as the capacity to identify novel and useful solutions to problems in the form of new products or services (Perry-Smith & Coff, 2011). Entrepreneurial creativity is required both by entrepreneurs that start a new business from scratch and by managers in existing firms that commercialize new appropriate solutions. Identifying these solutions is a process involving different stages (Figure 3.1). After having identified problems to solve (Amabile & Pratt, 2016; Brown, 2008), either accidentally or in a structured approach (e.g., focus groups, social listening) the venture creation process is structured in different stages (Figure 3.1). 1 First, new venture ideas are generated. These are preliminary and incomplete representations of a potential venture (Vogel, 2017). 2 Ideas must then be selected (Kier & McMullen, 2018) and refined into new venture concepts: simplified representations of the business model employed in a potential new venture (Vogel, 2017). 3 Finally, new venture concepts must be implemented (Amabile, 1997) when an opportunity or favorable combination of external and internal circumstances that make it feasible and desirable to implement a new venture concept (Vogel, 2017) arises. During new venture idea generation, entrepreneurs engage in creative thinking (Brown, 2008), while other skills are more solicited at other stages. For

When AI systems help to inspire creative new venture ideas 49 New Venture Idea

New Venture Concept

Idea Generation

Idea Selection and Refinement

Realized New Venture Opportunity

Idea Implementation

Information Processing

Individual Components of Creativity Skills in the Task Domain

Intrinsic Motivation

Creativity-Relevant Processes, (e.g. Cognition)

Generative AI Stimulation

Figure 3.1 The entrepreneurial creativity process

example, idea implementation requires the mobilization of resources such as a set of skills, knowledge, and prior experience (human capital), social connections (social capital), and access to funds (financial capital) (Clough et al., 2019). We consider entrepreneurial creativity as a fleeting, situation-dependent state as opposed to another stream of work that considers more creativity as an enduring personality trait measuring the human level of creativity (Gough, 1979; Hennessey & Amabile, 2009). Human beings are not equal in terms of innate creative personality, and we know that entrepreneurs tend to score higher in terms of openness to experience, which is linked to creativity as a trait (Zhao & Seibert, 2006). However, we assume that there are conditions that may influence the level of creativity. This assumption is in line with contemporary views of creativity (Amabile, 2017), which consider the positive impact of a stimulating environment on the level of creativity. To display entrepreneurial creativity during new venture idea generation, one must not necessarily be a creative genius. It is enough to generate a new venture idea that is sufficiently novel to allow some form of first-mover advantage and sufficiently appropriate, in the sense that it remains within a set of socially valued rules. These two characteristics are relevant as entrepreneurs must acquire legitimacy (Aldrich & Fiol, 1994; Lounsbury & Glynn, 2001) from stakeholders by convincingly arguing they can make a profit, complying with legislation or customer norms, and more.

50 Nathan Sorin and Margherita Pagani Idea generation stems from information exposure and processing, which is a cognitive activity. Cognition represents one of the three individual components of creativity (Amabile & Pratt, 2016): • creativity-relevant processes (e.g., cognition) • intrinsic motivation • skills in the task domain Different studies have investigated the influence of environmental factors on cognitive activity and the level of novelty and appropriateness of new venture ideas. Group mood (Perry-Smith & Coff, 2011), defined as temporarily shared emotion among team members, is one of these contextual factors. Interaction with physical objects (for example, drawing on a piece of paper) is also a documented source of unexpected realizations while creating (Klemmer et al., 2006). By generating content (text, image, or more), generative AI systems are a novel contextual factor that can shape the entrepreneurial creativity process by influencing cognition. However, there is a void of research measuring if generative AI systems can foster entrepreneurial creativity.

Generative AI systems that support entrepreneurial creativity Generative AI systems represent a category that includes all AI systems that use generative modeling and deep learning to create content such as images, texts, codes, audio, or even videos (e.g., Synthesia). By generating this content, we argue some generative AI systems are the next wave of “stimuli providers”: information technology–enabled tools that are designed to foster creativity by exposing individuals to relevant words, sentences, or pictures (Althuizen & Reichel, 2016). In Box 3.1 we illustrate three different cases of generative AI systems that foster entrepreneurial creativity by exposing individuals to relevant words or images.

Box 3.1 Illustrative cases of generative AI systems for entrepreneurial creativity Ideas AI: new venture ideas generated with GPT-3

https://ideasai.com/

When AI systems help to inspire creative new venture ideas 51 Exposure to ideas generated by other people is a documented way of influencing creative idea generation (Chen & Althuizen, 2022). AI developers have intuitively turned to create AI systems that generate synthetic new venture ideas. For example, IdeasAI by indie maker @ levelsio uses OpenAI’s GPT-3 to generate a long list of new venture ideas, which is updated daily (“Startup Ideas powered by OpenAI,”, n.d.). GPT-3 is a Generative Pre-Trained Transformer, in other words, it is a language model that is pre-trained with huge quantities of textual data. This kind of language model is called a few-shot learner, meaning it can perform many language tasks once it has been fed with a few examples of relevant outputs. GPT-3 has over 175 billion parameters (Brown et al., 2020). In this case, the model was provided with some examples of real new venture ideas backed by venture capitalists as data inputs. Outputs are short new venture ideas, and there are now thousands of automatically generated ideas available online. On top of the original training of GPT3, a human rating system of the AI ideas continuously improves the model. The intent behind this project is to provide inspiration to create, rather than encourage people to implement the raw ideas. This makes sense first because new venture ideas are not new venture concepts. They must, therefore, be refined by the individuals made of flesh and bone who have the ability and the motivation. Moreover, there is room for improving the ideas because they are imperfect and sometimes even useless. One example of a useless “idea” in the AI-generated list is simply a description of IGTV on Instagram, going as far as to include the actual name of the social network. This “idea” is a random piece of information about an existing company. Any human can understand the obvious lack of originality, unlike the algorithm’s probabilistic calculations. Other ideas generated with GPT-3, even if interesting, may show some bias due to the data used to train the system. For example, for many startup ideas, the AI system locates the hypothetical startup in San Francisco and names venture capital funds such as Andreessen Horowitz and Sequoia Capital. There is a bias in the training data that leads to an overrepresentation of the United States’ Silicon Valley compared to, for example, Kenya’s Silicon Savannah. A human user can spot such algorithmic biases due to data input incompleteness. This statement is especially credible when the human user has high expertise. Indeed, experimental findings in a setting where patent applications were evaluated (Choudhury et al., 2020) provide support for the argument. In our example, the user could have suggested the idea might have worked better in a different city.

52 Nathan Sorin and Margherita Pagani NotCo: food formulas for new products generated with Giuseppe

www.youtube.com/watch?v=5epu9v6T8TQ In some cases, creativity is mainly required to verbalize (put into words) the solution to a problem, especially if nothing similar exists. In other cases, creativity is crucial during the actual development of the product, especially if it involves the exploration of a technical solution space. For example, the unicorn company NotCo aims to replicate food products of animal origin using only plants. Such an idea is not very original on its own as many companies are in that space but generating new product recipes requires creativity. Creative recipes make unusual but appropriate associations between ingredients. To come up with their creative plant-based formulations, the team uses an AI-enabled platform named Giuseppe. This platform relies on its Latent Space Formula Generator (Patel et al., 2021). In a latent space, similar data points are closer. Based on nutritional, functional, and compositional data, their AI system generates plant-based recipes that have similar properties as animal products. For example, NotCo developed a plant-based alternative to milk with original yet appropriate ingredients such as cabbage and pineapple because that association was directly suggested by the AI system.

New Balance: new product designs with FootwearGAN

https://app.runwayml.com/models/Scccccry/FootwearGAN Managers in established firms also display entrepreneurial creativity when they launch new products and services. In the case of wellknown products, such as sneakers, creativity is required during the design process. While designers traditionally browsed through magazines for visual idea generation, New Balance designers use FootwearGAN (Valenzuela, 2020), a generative AI system available on Runway that generates sneaker designs. It is based on a generative adversarial

When AI systems help to inspire creative new venture ideas 53 network (GAN), GANs use two models (Goodfellow et al., 2014): a generative model that learns to generate plausible data and a discriminative model that learns to distinguish fake data from real data and penalizes the generative model for implausible results. With AI-generated sneaker designs, designers can discover a wide spectrum of images and select and improve visual cues that they find most interesting. Stimuli providers are not necessarily creative agents, but they stimulate creators. These systems initiate a particular train of thought, but a human must take over the heavy lifting to produce a better idea, refine it, and implement it. To understand better if and how we can use AI-generated content to inspire human-created new venture ideas, we first need to grasp what is happening at the cognitive level when individuals create. Let’s concisely cover cognitive theories of creativity.

Cognitive theories of creativity: stimulating the flexibility and persistence pathways According to Nijstad and Stroebe (2006), idea generation takes place within a two-step cognitive process. 1 In the knowledge activation phase, knowledge is retrieved from our longterm memory, responsible for our memories of events that happened in the past. 2 In the idea production phase, knowledge is available within our working memory, where information processing takes place. This is when we can recombine elements together to formulate ideas. Stimuli providers stimulate the activation and transfer of related elements from long-term memory to working memory. These systems then trigger the combinatorial processing that leads to creativity. Activated elements are related meaning-wise because our long-term memory is organized into clusters of items with semantic proximity (Santanen et al., 2004). For example, if you read the word “tree”, you are more likely to think about a leaf or a bird’s nest rather than an igloo. Not all stimuli have the same effects. Many parameters can influence their effectiveness such as the relatedness with the task at hand (Wang & Nickerson, 2019), the emotional connotation (Lewis et al., 2011), or the number of words that compose them (Garfield et al., 2001). Some researchers have suggested that creativity can appear with two different processes: the flexibility pathway and the persistence pathway (Nijstad et al., 2010). Stimuli can favor one or the other by design.

54 Nathan Sorin and Margherita Pagani The flexibility pathway requires high cognitive flexibility, which is the ease with which one can consider a new perspective. For example, imagining fusion cuisine recipes hinges on cognitive flexibility because it requires an ability to combine elements of different categories of culinary traditions. The persistence pathway requires cognitive persistence, which deals with sustained and focused cognitive effort. Imagining a creative recipe through cognitive persistence would lead to deeply exploring the possibilities provided by one culinary tradition to come up with a novel and appropriate combination of ingredients. These pathways, therefore, do not lead to the same types of ideas. If we consider a “space” of potential ideas, the flexibility pathway pushes creators to broadly explore the space. The persistence pathway, however, requires remaining in one part of the space but deeply exploring what is possible within it. Creative thinking, therefore, can be characterized both by breadth and depth of exploration.

How generative AI systems may influence entrepreneurial creativity along the different stages As we have seen earlier, generative AI systems can be trained for many different purposes and can stimulate the pathways to creativity in a variety of ways as well. Based on the type of synthetic content generated, we have identified three subgroups of generative AI systems (Table 3.1): 1 AI systems verbalizing (i.e., putting into words) new venture ideas 2 AI systems generating solutions by exploring a technical solution space 3 AI systems generating representations of products (e.g., visual designs) All these generative AI systems can be used for entrepreneurial creativity (identification of creative solutions to problems in the form of new products and services). However, not all these systems are used during new venture idea generation, where the focus is on identifying a preliminary representation of a venture that will deliver products or services. The first subgroup of AI systems appears to be relevant at that stage because these AI systems generate synthetic new venture ideas in the form of text, creating stimuli that are relevant to this task. We will argue next these AI systems stimulate the flexibility pathway to creativity. The other subgroups of AI systems generate synthetic outputs that are more focused in nature and, therefore, are only relevant if they are used by teams that already identified their business model but must still display high entrepreneurial creativity to develop new product formulas or designs to be brought to market.

When AI systems help to inspire creative new venture ideas 55 Table 3.1 Generative AI systems for entrepreneurial creativity Generative AI system

Entrepreneurial creativity stage

New venture idea AI systems generation verbalizing new venture ideas

AI systems generating solutions by exploring a technical solution space

New venture idea implementation

AI systems generating representations of products (such as visual designs)

New venture idea implementation

How the AI systems foster entrepreneurial creativity

Use cases

Help entrepreneurs generate a new venture idea (preliminary and incomplete representation of a potential venture) Once entrepreneurs already have a new venture concept (simplified representation of their business model), these systems can help identify creative new product “formulas” that can be brought to market Once entrepreneurs already have a new venture concept (simplified representation of their business model), these systems can inspire new product designs that can be brought to market

Ideas AI ChatGPT Notion AI

Giuseppe (food formulas) AlphaFold (accurate 3D protein structures for drug discovery)

FootwearGAN Midjourney DALL•E 2

Creating new product formulas requires an ability to tame complexity, as many variables come into play. Complex relationships between variables are taxing for our working memory and research has suggested humans are unable to comprehend relationships past four-way interactions (Halford et al., 2005). Generative AI systems that are specifically designed to deeply explore a solution space are, therefore, highly useful as they can handle higher levels of complexity and provide useful suggestions. These suggestions can then be evaluated and refined by human experts if necessary. In the case of new product designs, different kinds of AI systems can be useful to stimulate the human creator’s senses. Here, therefore, AI systems are used as stimuli providers. In Box 3.1, we have described the importance of exposure to visual stimuli and shown how sneaker design benefitted from AI-generated images. However, generative AI systems can also generate 3D

56 Nathan Sorin and Margherita Pagani

AI-Generated New Venture Ideas

KNOWLEDGE ACTIVATION PHASE

IDEA PRODUCTION PHASE

Less Accessible Knowledge Activated

Cognitive Flexibility

Average Idea Creativity

Number of Creative Ideas

Figure 3.2 AI-inspired entrepreneurial creativity during new venture idea generation

representations, audio, and even video. Perhaps it will become mainstream to use even more sophisticated AI-generated sensory experiences to support creative product design in the future. These systems are also becoming increasingly interactive (e.g., Midjourney and DALL•E 2 enable users to generate product representations based on wild textual prompts such as “an armchair in the shape of an avocado”). Both pathways to creativity may be stimulated, depending on the specific AI system and the task. While all these systems have interesting implications, this chapter aims to shed light on how generative AI systems can foster entrepreneurial creativity at the new venture idea generation stage. Consequently, we will now focus on the use of generative AI systems that verbalize new venture ideas, in the light of the dual pathway to creativity model. We believe these systems do not support the intense, deep thought processes necessary for cognitive persistence to occur. Indeed, new venture ideas are formulated in a few sentences. However, to build this concise verbal output, the AI systems rely on an extremely broad knowledge base. For example, GPT-3 is trained on the Common Crawl corpus, which contains petabytes of data collected on the web over several years. No human being has gone through all this text. Consequently, generative AI systems can generate very diverse synthetic new venture ideas that can take us in some way by surprise. Such stimulation should activate less accessible knowledge that can then be used by entrepreneurs during idea production. Conversely, unstimulated individuals usually follow the path of least resistance and use easily accessible knowledge (Nijstad & Stroebe, 2006; Ward, 1994). The activation of less accessible knowledge leads to cognitive flexibility (Chen & Althuizen, 2022). Consequently, generative AI systems orient our attention in a direction we had not previously thought about; and hence, we argue they trigger the flexibility pathway to creativity. We expect cognitive flexibility will influence positively two creative outcomes: average idea creativity and the number of creative ideas (Althuizen & Reichel, 2016) (Figure 3.2).

The role of AI-enhanced cognitive flexibility on creative new venture idea generation To explore the mechanism through which AI systems, when used as stimuli providers, may foster idea generation, we conducted two experimental studies

When AI systems help to inspire creative new venture ideas 57 (Sorin & Pagani, 2022). The objective was to check if the use of AI-generated new venture ideas may have a direct positive effect on the cognitive flexibility of participants and if this enhanced cognitive flexibility, in turn, affects the average creativity and creative productivity (number of creative ideas). Average creativity refers to the average level of usefulness and novelty of the ideas generated (Amabile & Pratt, 2016). A new venture idea is “useful” if implementing it can create value for customers and if the implementor can capture value. An idea is novel if it is different from other ideas entrepreneurs have come up with in the past. The number of creative ideas refers to the number of ideas that are over a determined threshold of creative quality. Triggering the flexibility pathway In the first experiment, we asked 90 participants to answer an incubator’s call for applications by submitting new venture ideas. This assignment left them free to think about the domain as we wanted participants to be free to explore. Providing a scenario allows participants to role-play and feel invested in the task. All participants completed this task; one group could use AI-generated ideas as a source of inspiration and one group could not. Ideas were generated with GPT-3. Because of the risk of cognitive overload, we did not provide access to the thousands of ideas generated by GPT-3. Instead, we provided ten ideas for each participant in the AI-enabled group, randomly extracted from a larger set of AI-generated ideas. We are limited to ten ideas as this number approximately corresponds to what our working memory can process at once (Miller, 1956). In this study, we wanted to see if AI systems could inspire entrepreneurial creativity by activating the flexibility pathway. With this goal, we designed specific conditions that were more likely to provoke the use of the flexibility pathway. One of the conditions was the number of AI-generated ideas as more ideas would push individuals to consider many different perspectives and consequently foster cognitive flexibility. The ideas suggested by the AI system were not limited to one specific topic, allowing for the activation of less accessible knowledge. Participants were given the freedom to fully explore the space of possibilities, with no restrictions, which made it possible to observe enhanced cognitive flexibility. The experiment was run online on the same interface in both groups. In total, participants generated 208 new venture ideas. We measured the average creativity, the number of creative ideas generated, and the cognitive flexibility of each participant. We applied the consensual assessment technique (Amabile, 1982) that considers an idea creative to the extent that appropriate observers (experts) independently agree it is creative. Although not necessarily flawless, this technique provides a way to quantify creativity. Building on this work, we used a scale provided in the scientific literature (Besemer & O’Quin, 1986) that decomposes creativity into a novelty dimension and a

58 Nathan Sorin and Margherita Pagani usefulness dimension to measure the creativity of the new venture ideas. We used three items to measure novelty (newness, uniqueness, unusualness) and two items for usefulness (feasibility, operability). We asked expert judges to rate ideas independently. Every idea was evaluated at least twice. Our expert panel included two heads of entrepreneurship university tracks; one management professor, who was also part of a real expert panel for a large bank accelerator program; a director of an intrapreneurship program in a large telecommunications firm; an intrapreneurship consultant in one of the big four auditing and consulting firms; and a serial entrepreneur. Profiles were voluntarily heterogeneous. In the end, the interrater agreement was deemed acceptable. Each participant was awarded an average creativity score corresponding to the average of the creativity evaluations obtained for each idea generated. To measure the other outcome we were interested in, which is the number of creative ideas generated by each participant, we considered the number of ideas that were evaluated as above the overall median for both the novelty and the usefulness dimensions of creativity. Finally, to measure cognitive flexibility, consistent with prior research (Althuizen & Reichel, 2016), we measured the breadth of exploration. We grouped all ideas into 15 overall topic clusters, or semantic categories, such as “cooking and catering”, “entertainment”, or “message platforms and social networks”. Then, we counted the number of categories for which each participant generated at least one idea. This enabled us to have an objective measure of how broadly everyone explored the space of entrepreneurial possibilities. When AI systems influence creative productivity through cognitive flexibility Our results for Study 1 confirmed that the use of generative AI systems had a statistically significant effect on cognitive flexibility. When testing our prediction, we found that, on average, participants in the AI group displayed a breadth of exploration score of 2.32, while the control group’s average was 1.60. The gap is relevant considering the time spent generating ideas was short (around ten minutes). However, results were mixed when investigating the role of AI-enhanced cognitive flexibility on creative outputs. We found no effect on the average creativity of the ideas generated. We observed that individuals in the control group were on average more creative as they had a creativity score of 4.35/7 versus 4.23/7 for the AI group. Regarding the number of creative ideas generated, the results were in line with expectations. When the use of generative AI triggers the flexibility pathway, individuals tend to produce more ideas that are above our creative threshold. The AI group also generated more novel ideas (1.38 vs 1), more useful ideas (1.18 vs 1), and more ideas in general (2.71 vs 1.91). Therefore, the use of AI seems to favor broader exploration, leading to higher creative

When AI systems help to inspire creative new venture ideas 59 productivity (number of creative ideas) but not enhancing the average creativity of new venture ideas generated. When AI systems do not stimulate cognitive flexibility but foster average creativity In Study 2, we manipulated cognitive flexibility by setting conditions that were less likely to stimulate it. Accordingly, we asked all participants ((n=117) entrepreneurs) to generate new venture ideas related to the use of technology in education, restricting the freedom participants had to explore. AI-generated ideas that were relevant to the topic were used as stimuli, making them less diverse. Finally, we reduced the number of AI-generated ideas provided to participants from ten to four. The experimental design was almost identical to Study 1. Also this time, all 183 ideas were evaluated by three experts (one head of a startup accelerator and two management researchers), and discrepancies were resolved to attain a high level of agreement. Cognitive flexibility was measured in the same way as in the first study. In this second study, the use of AI-generated new venture ideas as a source of inspiration did not affect cognitive flexibility. The average breadth of exploration score was of 1.39 in the AI group and 1.38 in the control group. However, we found that the use of generative AI had a direct positive and statistically significant effect on the average creativity of the ideas generated. On average, the AI group had a creativity score of 4.86/7 compared to 4.52/7 for the control group (Cohen’s d = 0.47). This time the effect on the number of creative ideas generated was not statistically significant.

Discussion of the findings emerging from the experimental studies In our studies (Sorin & Pagani, 2022), we found that generative AI systems may stimulate creative new venture idea generation in two different ways: 1 When AI-generated ideas increase cognitive flexibility, we observe an indirect positive and significant effect on the number of creative ideas generated by the individuals. 2 When AI does not affect cognitive flexibility, we observe a direct positive effect on the participants’ average creativity scores (Figure 3.3). Enhanced cognitive flexibility enabled individuals to reach different areas of the solution space and generate a higher quantity of creative ideas. However, average creativity was not fostered. Our interpretation of this unexpected result is that the stimulation of cognitive flexibility harmed idea elaboration or the amount of detail provided for each idea. Idea elaboration is sometimes used to assess creativity (Torrance, 1968).

60 Nathan Sorin and Margherita Pagani STUDY 1 High Cognitive Flexibility Scenario

STUDY 2 Low Cognitive Flexibility Scenario

AI-Generated New Venture Ideas

AI-Generated New Venture Ideas

Cognitive Flexibility

Average Creativity of New Venture Ideas

Number of Creative New Venture Ideas

Figure 3.3 The influence of generative AI systems on entrepreneurial creativity during new venture idea generation

Our interpretation is indirectly supported if we look at the number of words used to express each idea. When generative AI stimulated cognitive flexibility, ideas were on average 63 words long compared to 105 in the control group. In Study 2, where cognitive flexibility was not stimulated, ideas were on average 114 words long in the AI-enabled group versus 99 words long in the control group. As the time given to generate new venture ideas was constrained, this lower elaboration may be linked to a lower time dedicated per idea as cognitive flexibility and the total number of ideas generated were simultaneously stimulated. Another related argument is that stimulating cognitive flexibility may lead to superficiality (Nijstad et al., 2010). Our measure of cognitive flexibility is the number of categories for which the participants generated at least one idea. Under high time constraints, participants did not have time to generate many ideas in each category explored and might, therefore, have stated the more obvious solutions that immediately came to mind but were not necessarily very creative. Restricting the scope of the idea generation task in Study 2 has made AIgenerated ideas more related to one another. Therefore, participants were

When AI systems help to inspire creative new venture ideas 61 more likely to make links between AI ideas. Moreover, AI-generated ideas were more likely to be related to the participants’ thought processes. In Study 1, ideas could be completely unrelated to the areas of the topic space the individuals were considering. We know from prior work that stimuli relatedness to the creative task enhances idea usefulness (Wang & Nickerson, 2019). In Study 2, idea average usefulness was significantly influenced by AI use, while idea novelty, the other dimension of creativity, was not significantly influenced. In conclusion, participants might have benefitted more from the ideas because they were more related and bridges could be envisioned between AI ideas. They had less freedom to explore but had time to elaborate ideas using relevant AI-generated ideas at their disposal. With these results, we contribute to creativity research by showing that generative AI systems are a novel situational driver of creativity that leads to different outcomes depending on whether cognitive flexibility is stimulated or not. We also contribute to entrepreneurship research by specifically studying the influence of generative AI systems on entrepreneurial creativity when prior work has so far overemphasized the symbolic, procedural rationality of AI systems, thus ignoring generative AI systems (Townsend & Hunt, 2019). We addressed this gap by integrating insights from the literature in information systems as well as psychology and by empirically testing our predictions. Practical implications Here are some recommendations for individuals planning to use generative AI systems during intentional new venture idea generation. If the goal is to push the future entrepreneur or entrepreneurial team to explore a wide variety of ideas, exploring the space of possibilities broadly, then using AI-generated ideas to enhance cognitive flexibility might be effective. However, we recommend that entrepreneurs keep in mind that this may decrease idea elaboration. Therefore, this use of generative AI will require higher idea refinement. At this point, other types of AI systems could be helpful to gather information about a particular product market space. This is useful to prepare oneself before refining the ideas by building the appropriate knowledge base (Amabile & Pratt, 2016). In some cases, other AI systems can also be used to evaluate the quality of ideas and validate them. Indeed, AI systems can sometimes predict how customers will react to a new offer (Chalmers et al., 2021). If the targeted product market space is already identified, and the objective is to generate one highly creative idea, then a few context-specific AIgenerated ideas will help achieve the best results, stimulating knowledge retrieval and helping make better connections between concepts without scattering attention.

62 Nathan Sorin and Margherita Pagani Conclusion Finding creative new venture ideas is far from easy. Motivation, knowledge, and expertise are important, but the ability to imagine is essential as well (Kier & McMullen, 2018). We believe that examining how we can cultivate entrepreneurial creativity is not only interesting for its own sake but is also critical if we want to overcome the many grand challenges we need to cope with and that await us in the future. Perhaps more educators should consider how they can cultivate their students’ creativity in their classes including creativity in high school curricula. In this chapter, we showed that generative AI systems are one potential tool that can nurture creativity.

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4

How AI can foster business creativity Margherita Pagani and Renaud Champion

Introduction How can AI foster organizational creativity inside a company? Applying AI to organizational creativity can lead to the development of new products, ideas, collaboration methods, and ways of thinking (Pagani & Jablokov, 2022). AI can already help crumple organizational silos and allow the construction of a new kind of creative “connective tissue” across organizations and business processes. But AI and emerging technologies can also accelerate and expand creativity in the organization by suggesting new directions, inspiring new ideas, and even creating new products. Instead of replacing human creativity, AI can create an exponentially more enjoyable and more productive experience. This concept is at the center of the organizational metamorphosis considered in this book. Through this chapter, we aim to explore the following research question: how may the adoption of Artificial Intelligence trigger business creativity? We first present a theoretical review of the concept of business creativity and the impact of AI on organizational creativity as a possible way to predict different dimensions of creative output. Using the concurrent engineering framework, we conducted several semi-structured interviews with managers and leaders applying AI systems in creative and complex tasks to explore how AI systems may foster business creativity. We wish to thank the visionary leaders who have accepted to discuss the topics presented in this chapter, namely Luc Julia (Chief Scientific Officer, Renault Group), Bruno Bonnell (General Secretary for Investment, France 2030, and AI entrepreneur) Silvano Sansoni (General Manager, Global Digital Sales, IBM), Pierre Collinet (PhD/ MD Gynecologist Surgeon) In this chapter, we summarize the findings from these interviews, highlighting avenues for future research and discussing implications for management seeking to foster creativity using AI systems and innovation in the workplace. DOI: 10.4324/9781003287582-7

66 Margherita Pagani and Renaud Champion What is business creativity? Business creativity is a way of thinking that inspires, challenges, and helps employees to find innovative solutions and create opportunities out of problems. It can be defined as a mental process of individuals that takes place within organizations to develop the company’s product, service, or management methods. Woodman et al. (1993) define creativity as “the creation of a valuable, useful new product, service, idea, procedure, or process by individuals working together in a complex social system”. Some companies show a high level of creativity expressed through innovative products able to create new needs and open new markets (for example, Apple or Walmart as described in Box 4.1) or provide innovative business models (i.e., the case of Uber or Airbnb). Sometimes we may risk confusing a visionary with a creative company, but these two characteristics are different, even if strictly related. Business creativity is undoubtedly the starting point of innovation (Woodman et al., 1993), but it is also well distinguished as it plays an essential role in the first stage of creation, that is, the initiation stage (West, 2002).

Box 4.1 AI and business creativity in Walmart Walmart At Walmart, an AI team of 80 data scientists, data analysts, and data engineers are in charge of building algorithms that will power the decisionmaking of merchants into core areas like assortment pricing, inventory management, financial planning, and all aspects of merchandising.

How is Walmart using artificial intelligence to improve its business? Walmart wants to use AI to serve consumers better. To do this, the company partners with businesses closely and takes business stakeholders along the journey. It’s not just about algorithms, it’s about business because the goal of AI is to improve the business. I’m responsible for all the machine algorithmic developments in core areas of merchandising, which include, how you price something, how you select the right assortment, replenishment strategies, forecasting, and planning. So all the core aspects of merchandising are what we are trying to use machine learning and AI towards. (VP of Machine Learning, Walmart)

How AI can foster business creativity 67 Creative Challenge Considering the store side, Walmart collects information about the transaction made but not the history of a consumer, which products the customer has picked up, and why the decision has been made. The job of algorithms is to infer as opposed to directly learning from the data. The key challenge for the AI team in Walmart is to create algorithms that make inferences (as opposed to directly learning from data) and then translate it into actionable insight useful to make a forwardlooking decision. How does that feel when you’ve gone from a world where everything is highly quantified to one where everything is abstract and you’re still asked to make a decision?

There is no doubt that business creativity is an essential element in today’s world, both for consumers and businesses (Mehta & Zhu, 2016). Consumers engage in creative behaviors to satisfy their needs and solve consumptionrelated problems (Burroughs & Mick, 2004; Burroughs et al., 2008), gain enjoyment (Dahl & Moreau, 2007), and relax. Meanwhile, many businesses achieve success because of their consumers’ and employees’ ability and desire to be creative (Mehta et al., 2017).

Factors sparking business creativity When companies adopt a strategic decision to adapt to changing environments, they require creativity as they need to define a compatible organizational design; introduce innovation-inducing business strategy; change management styles, organizational structures, and management practices; and develop more effective management of innovations. Research shows that business creativity is the result of multiple factors that must converge for it to spark. We identify two main drivers: (a) individual factors or the characteristics of the organization members fostering creativity and (b) organizational factors or the characteristics of the organization that facilitate and foster employee creativity.

Individual factors fostering creativity Several individual factors may influence business creativity such as intrinsic motivation, identified as a necessary but not sufficient condition for creative outcomes (Amabile, 1996; Taggar, 2002). Engaging in creative activities has a role in promoting employee creativity (Amabile, 1996; Amabile & Pratt, 2016). Moreover, individual factors such as the employee’s personality,

68 Margherita Pagani and Renaud Champion cognitive style, extroversion, fluency of thoughts, and emotional cognition (Gupta & Banetjee, 2015) as well as the level of involvement (Burroughs & Mick, 2004), analogical thinking (Dahl & Moreau, 2007), and life experiences (Maddux & Galinsky, 2009) may play an important role. These individual factors may be amplified when we refer to the level of a group. Factors such as group cohesiveness, group composition, group interactions, group leadership, cultural factors, ethnic diversity, gender diversity (Paulus & Brown, 2003), and response to recognition for creative achievement (Harrison et al., 2022) were found to shape their subsequent creative outcomes. Creative employees show more commitment and proactivity in exploring new opportunities helping the company to leverage them for better results. For this reason, creative employees represent a great asset to fostering business creativity (Gupta & Banerjee, 2015). Organizational factors fostering creativity Five different organizational factors are recognized in the literature as drivers to enhance and foster business creativity in the work environment within organizations (Andriopoulos, 2001): 1 An open organizational climate, characterized by interactions with small barriers, many stimuli, freedom to experiment, and the possibility of building on earlier ideas (Feurer & Wargin, 1996), can foster creativity. 2 A democratic and participative leadership style (Amabile et al., 2005) is characterized by trust between leader and subordinates and information sharing. Also, participation in decision-making and perceptions of autonomy are vital preconditions for creative outcomes (Amabile et al., 2005). 3 Organizational culture, or a system of shared values that produces normative pressure inside organizations, affects motivation and develops an “innovative” (divergent and learning) and “supportive” (empowering and caring) culture (Wiener & Vardi, 1990). 4 Resources and skills including attracting, developing, and retaining creative talents, are crucial for fostering innovation in an organization. This necessitates providing systematic training, motivating individuals to generate new ideas, and allocating sufficient time for work and project design (Elsbach & Hargadon, 2006). 5 The structure and systems of an organization can enhance creativity when characterized by a flat structure, fair supportive evaluation of employees, and rewards for creative performances (Andriopoulos, 2001). Even if these factors are extensively explored in the literature, a void of research emerges on the effect of Artificial Intelligence on all the factors listed here.

How AI can foster business creativity 69

Exploring the impact of Artificial Intelligence on business creativity Several studies have demonstrated that AI capabilities, when properly developed, can enable companies to improve their business creativity and the resulting performance gains (Mikalef & Gupta, 2021). These AI capabilities are the abilities of a firm to select, orchestrate, and leverage its AI-specific resources, and they are identified by Mikalef and Gupta (2021) within three main categories: (a) technical resources (such as data, technology, basic resources), (b) human resources (technical skills, business skills), and (c) intangible resources (inter-departmental coordination, organizational change capacity, risk proclivity). These capabilities may also have a positive effect on organizational creativity by improving knowledge sharing in the company or discovering new knowledge through data mining. However, to influence organizational creativity, companies need to take a holistic approach to implement AI within their organizations, which considers more than just the technical aspect of it (Mikalef & Gupta, 2021). Considering this holistic approach, we explore in this chapter the impact of Artificial Intelligence systems on business creativity. We conducted several exploratory interviews with managers and key leaders in the field and summarize the reflection by adopting the “three-dimensional concurrent engineering” framework (Fine et al., 2002), which adds value chain engineering to augment the traditional two-dimensional concurrent engineering of products and processes (Fleischer & Liker, 1997). Adopting this framework, we specifically investigate three main dimensions in which AI systems have to enhance business creativity (Figure 4.1): 1 Stimulates continuous and ongoing innovation allowing new product/ service design. 2 Allows new optimized processes and procedures across teams. 3 Helps organizations to be more creative by facilitating and increasing organizational collaborations (sharing of resources) and alliances.

NEW PRODUCT DESIGN

NEW PROCESS DESIGN

NEW COLLABORATIONS AND ECOSYSTEMS

Figure 4.1 Three dimensions of analysis of the concurrent engineering framework

70 Margherita Pagani and Renaud Champion AI in new product design Accelerating discovery The first way AI and emerging technologies (quantum computing, cloud, IoT, mobile, edge points) influence new product design is by allowing accelerated discovery, assisting scientists and engineers in their creative process. AI makes possible an analysis of a huge amount of books, documents, publications, and patents to accelerate the discovery of new types of products and services. In the aerospace domain, AI may help in designing “light” airplanes with less carbon consumption and less fuel consumption based on materials that must meet several environmental criteria. Before the creative phase, scientists and engineers have to develop a very important phase of research and documentation from many teams of experts. This phase used to be very long and difficult with the hundreds of thousands of articles, patents, and publications that needed to be ingested. Thanks to AI, engineers, consultants, and architects are getting information from different sources in real time. This is the first step of creativity as it allows a very scientific, accelerated discovery. In the pharmaceutical industry, the creation of drugs requires a very long time (many years) and several clinical essays. Nowadays, Artificial Intelligence may suggest some molecular combinations to researchers in a short time accelerating discovery. In this case, AI systems can be classified as assisted augmented AI. Accelerated Discovery is always about augmented AI which allows us to create more than a hundred new products that are now in the beta version. So, this is the way we conceive AI in order to assist scientists and engineers to quickly reach the innovation point while saving time and money. (Silvano Sansoni – General Manager, Global Digital Sales, IBM) Deep product learning Artificial Intelligence may also accelerate discovery not only by assisting in the research phase but also through deep product learning using Generative Adversarial Networks (GAN). Given a training set, this technique learns to generate new data with the same statistics as in the training set. The idea is to train two neural networks competitively, where the first network generates fake samples and the second network discriminates whether the samples are real or fake. For example, a GAN trained on images can generate new images that look at least superficially authentic to human observers, having many realistic characteristics. GANs are used to generate new products from existing product data. In this case, as the generative and discriminative models

How AI can foster business creativity 71 converge together, the generator is able to replicate realistic shoes. In this way, AI can be used to fill the gap that exists between consumer desire and product offering. Generate new solutions Some more autonomous models of generative AI systems allow generating new solutions and new designs without human intervention. This technique is called generative Artificial Intelligence and or any type of Artificial Intelligence that uses unsupervised learning algorithms to create new digital images, video, audio, text, or code without human intervention. As described in Chapter 2 generative AI systems (such as GPT3, ChatGPT, DALL•E, and many others) are already used by artists to create new art crafts, and they help designers to foster new ideas and solutions, bringing new designs to market. In 2019, renowned designer Philippe Starck collaborated with Kartell and Autodesk on a generative AI project that resulted in the production of the “A.I.” chair. The AI system received a simple design challenge from Starck (to support the body with the least amount of material and energy possible) and, through what Starck called “a new language, a new type of exchange”, the system iterated and learned until the final product was crafted. Many companies are already using generative AI systems technologies in their product development strategies. The more data a comp any has, the better it can predict what consumers will want to buy. This information allows businesses to make more accurate sales forecasts and create products that more accurately meet customer demands. Machine learning algorithms analyze consumer trends and preferences as they develop new products, allowing companies to successfully guess what their audience wants before anyone else. Using generative AI systems in product development helps companies save time and money, as they do not have to go through trial-and-error iterations before finally releasing a product that people want. The product is created at the outset, avoiding any changes along the way. However generative AI systems that are able to produce art and visuals, such as AI-DA robots, DALL•E, Open AI, and GPT, are created by humans, and real creativity can be observed in the human who generates the prompt. These generative AI systems are impressive because of the way people are using them but they are a combination of existing ideas. So real creativity can be seen in the prompt or the user input for AI Art Generators. This could be a phrase or line of text that details the elements which the AI uses in producing an image. (Luc Julia – Chief Scientific Officer, Renault Group)

72 Margherita Pagani and Renaud Champion Other generative AI systems (such as Codex) can generate code. This means that if you are going to develop a game and you say, “Show me on the screen a rocket that is going to go from left to right”, the system can generate the code to write the game. Also, in this case, it is the prompt, or how to use the tool, that is key. The output needs to be adjusted by the human who must do several iterations because the system is borrowing from a lot of different places, and pieces of code and many inconsistencies may occur in the variables that are in the code. This requires reviewing the code and checking the variable names, for instance, to make them consistent across all the different pieces of code that have been used by Codex to realize a very simple program until you have some very complex prompts. All these generative AI tools allow generating many new ideas from your wild brain. The results of the generative AI systems may show you something that you didn’t even think about when you generated the prompt and so this is where the creativity is, and this is where you know you can get some ideas from those systems. It is like what happens with the blender if you put something, you push the button, and then what you get is something that you didn’t even imagine but it’s not that bad. So this is a little bit what you’re going to get potentially with generative AI so to have ideas of new products. (Luc Julia – Chief Scientific Officer, Renault Group)

Increasing data processing abilities: speed, power of analysis AI systems may also inspire employees and help them to augment their creativity for new products design requiring a high level of complexity. We can consider as an illustrative case chemical and biomolecular product design that benefits from the use of computer-aided solution strategies and computational power to efficiently solve problems at various scales as the complexity and size of problems grow. In this context, new modes of computation such as quantum computing influence the creative solutions in product design. This is the case also for industrial settings (i.e., railway interlocking), where AI allows augmenting human intelligence and enables a new solution for very complex problems. New quantum systems (Figure 4.2) allow also to realize real-time market tests for innovation projects. Algorithms reach and react in real time to the professionals involved in an innovation project (e.g., future users, customers, partners) all over the world. When a new concept/idea is prototyped, the AI system can explore and analyze a huge amount of data on the web and provide directions for potential applications fostering product/service creativity. It works both ways. It can be either for a business looking for innovations or for an innovation that can be exploited in different industries. In this way, the entrepreneur can be exposed to all the possible innovations, additional resources, and income.

How AI can foster business creativity 73 I decided to implement this AI system because I want to be able to see, when I am financing an innovation, what are all the possible applications I may have. (Bruno Bonnell – General Secretary for Investment, France 2030)

Figure 4.2 IBM Quantum System One Flexi cables Source: IBM Research

Table 4.1 summarizes the four ways Artificial Intelligence stimulates continuous ongoing innovations in new product design. Table 4.1 The impact of AI in new product design Impact of AI on new product design

Benefits

Accelerating discoveries

AI assists the research phase by analyzing a huge number of books, documents, publications, and patents to accelerate the discovery of new types of products and services. Using generative adversarial networks (GAN), AI can be used to fill the gap that exists between consumer desire and product offering. Using unsupervised learning algorithms, it is possible to create new digital images, video, audio, text, or code without human intervention. Using quantum computing, it is possible to generate complex solutions thanks to enhance data processing and speed.

Deep product learning Generating new solutions and designs Increasing data processing abilities

74 Margherita Pagani and Renaud Champion New process design A process is a series or set of activities that interact to produce a result. Many processes are done by humans and are repetitive tasks. When we consider the implementation of rule-based AI systems and computer science in these repetitive processes through automation, they may significantly help optimize the sequential set of activities, realizing more efficiencies. In this way, humans can focus on rearranging engineering and investigating new ways of working. This is the case for any process that has repetitive and frequent tasks that need to save time and be efficient (i.e., security or supply chain) In the security domain (i.e., cybersecurity) when you are in a hybrid cloud environment, which means that you have the infrastructure in the cloud, there are new processes (AI-generated processes) that are established thanks to fast learning AI and aim at securing the organization. During the pandemic, the number of instant connections that we had, was incredibly higher compared to the past, and alerts were analyzed in real-time by AI. In the security space, AI continuously creates new processes that were suggested to engineers to make them work in a new way (i.e., instead of analyzing millions of connections AI was able to deal with millions of simple alerts allowing engineers to focus only on complex issues). Thanks to this massive at-scale connection in the pandemic, we were able to test, a brand-new environment for AI. So, for example, this is used today by financial institutions to detect fraud or major risks or complex alerts. (Silvano Sansoni – General Manager, Global Digital Sales, IBM) All these AI systems applied to specific processes are called expert systems or computer applications developed to solve complex problems in a particular domain, at the level of extraordinary human intelligence and expertise. They allow several functions and can advise, instruct, and assist humans in decisionmaking. In healthcare, AI improves the accuracy of diagnosis, explains and predicts results better, and helps patients in all the phases of the recovery, improving the quality of the treatment. Moreover, they are already used also to assist doctors in microsurgeries. Even if these expert systems are incapable of substituting human decisionmakers, possessing human capabilities, producing accurate output for the inadequate knowledge base, and refining their knowledge, their adoption can help humans better do these tasks. We identify two main dimensions through which AI systems may help process creativity, one at the level of the tasks and the second at the level of the humans (Figure 4.3): • Dimension 1 (horizontal axis) – AI systems may replace humans or become a collaborator augmenting the human.

How AI can foster business creativity 75 • Dimension 2 (vertical axis) – Automation vs. augmentation of the task: AI systems can replicate the existing task or augment it. Based on these two dimensions we distinguish four types of impact of AI on processes.

Augment tasks

Cognitive insights AI allows to gather data from the environment, and build knowledge out of it to stimulate creative solutions

Simplify tasks

Process automation automation of digital and physical tasks using process automation that provide new and useful (creative) solutions

Replacing humans

Cognitive interactions (M-M, M-O, M-E) synergistic integration of mechanics, electronics, control theory, and computer science within product design and manufacturing, to improve and/or optimize its functionality Cognitive engagement use natural language processing chatbots, intelligent agents, and machine learning to engage customers and employees

Augmenting human collaboration

Figure 4.3 The role of AI in process design

Process automation When AI systems are used to simplify repetitive tasks and replace humans, we have automation of digital and physical tasks using AI process automation. The manufacturing process is benefiting from AI algorithms and robots, which are used to quickly and accurately assemble products. Instead of having people manufacture individual parts and then put them together, intelligent robots can use machine learning to create the product itself based on a design blueprint provided by workers. The result is faster production time and more efficiencies. Moreover, we can observe creativity in predictive maintenance. AI systems allow forecasting when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Additionally, the system can automatically trigger reorganization activities if pieces of equipment are broken. Similarly, applications of AI systems for predictive maintenance allow manufacturers to increase efficiency by identifying potential downtime and accidents by analyzing sensor data. Cognitive insights When AI systems augment complex tasks, replacing humans, we have the case of cognitive insights. This refers to all tasks that are related to sensing, gathering data from the environment, and building knowledge out of it. An interesting example has been conducted by Autodesk in its research lab in Toronto on how to redesign space in unimaginable ways to reduce the risk of virus contagion during the pandemic. Sensors and cameras were able to collect

76 Margherita Pagani and Renaud Champion data regarding inflow, temperature, and people’s behaviors in the space. These data fed an AI algorithm able to simulate the congregation patterns and provided the unimaginable design of the space. https://m.youtube.com/watch?v= YZbktkOf4Vw&feature=youtu.be This is also the case with the adoption of Artificial Intelligence in marketing processes to enable marketers to better understand their target audiences by quickly identifying past buying history, buying preferences, credit scores, and other common threads through analytics. Artificial Intelligence supports project managers in automated risk management (predicting and foreseeing future risks by monitoring and controlling performance changes, deadlines, and solutions), resource management, and predictive and controlling analytics. AI systems provide cognitive insights and allow managers to be more creative, completing projects more efficiently and on time and increasing organizational collaborations (sharing of resources) and alliances. When we consider HR, the use of AI can create new processes for defining the performance of employees, suggesting training and salary increases. This is just assisting. But, it allows also to have a 360°-view of the company’s competencies and then, based on that, the AI, through accelerated discovery, can suggest the ideal training for each employee or the best retention strategies to prioritize employees that are in a specific context (i.e., retention risk). When predictive analytics are applied to marketing or finance processes, they allow the generation of a pattern from data or information within the organization to find and detect possible risks and benefits. And this is done faster and more accurately but leaves space for human creativity in finding the best solution. Cognitive engagement When AI systems simplify tasks augmenting human collaboration, we refer, for example, to all the uses of natural language processing chatbots, intelligent agents, and machine learning to engage customers and employees. For instance, chatbots able to mimic how humans have conversations via voice commands and text chats offer an easier way to provide efficient customer service. In this case, the human can focus on answering more complex questions while leaving the chatbots the more standard and routine tasks. In many instances, companies combine expert systems with human creativity such that issues beyond the bot’s capacity are transferred to a human agent. Artificial Intelligence superior in the customer service division is its ability to target specific consumers and cater to their particular tendencies. Delivering a personalized experience to customers encourages brand loyalty and keeps the business booming.

How AI can foster business creativity 77 Also, it allows HR departments the connection of talented and intelligent employees (teams) across organizations and departments, eventually creating new methods of sharing and exchanging valuable information. Cognitive interactions When AI systems improve complex tasks augmenting also human collaboration, we have the case of cognitive interactions. This refers to the synergistic integration of mechanisms, electronics, control theory, and computer science within product design and manufacturing to improve and/or optimize its functionality. This is the case of AI applied to manufacturing processes or when robots can coordinate and control factory machinery, effectively performing many of the tasks that human workers would usually do. These robots can collect data to improve their performance and ensure efficiency. AI is also already making enormous differences in how athletes train. Companies like Seattle Sports Sciences and California-based Sparta Science, for instance, provide teams with machine-learning tools that analyze athletes’ movements to improve their training and even predict injuries.

New organizational collaborations The third dimension we consider is how new collaborations and ecosystems foster business creativity. We refer to the emergence of a collection of technologies and initiatives helping organizations to develop creativity through collaborative convenience. They include the latest developments in extended reality (XR) – an umbrella term for virtual reality (VR), mixed reality (MR), and augmented reality (AR) – as supplemented by artificial intelligence (AI), cloud computing, quantum computing, and other supporting technologies. These technologies already allow companies to develop creative customer experiences but also new ways to collaborate. AI allows the development of new working methodologies. Several AIbased working methodologies have been proposed by software providers (e.g., Collaborative Product Development by IBM) that help manage daily collaboration among different ecosystems involved in the different phases of product development (from the design phase to the production). The variety of ecosystems involved sometimes creates a lot of confusion, and the risk that they may not be aligned may cause an increase in time to market. The benefit of AI in favoring new working methodologies allows for finding creative ways of collaboration with more efficient time to market. New security issues need to be addressed to make this ecosystem sustainable. We think that there are some ethical and transparency rules that do not exist today and that the scientific community and the engineering community

78 Margherita Pagani and Renaud Champion must define as we go to the next step. What is possible with art and media is not possible in every domain because there may be risks for humans. Think about the pharmaceutical industry, think about the security domain, think about the aerospace domain. We fully promote the ecosystem, but we believe that this ecosystem must have strong governance. What we see today is a lot of focus on the near term, but still reluctance in developing the long term because of numerous threats such as governance, creation rules or several ongoing practices. (Silvano Sansoni – General Manager, Global Digital Sales, IBM) The processes in the metaverse When we talk about AI today, we need to consider also the metaverse that potentially could represent the digital twin of the real world, allowing companies to optimize what they are doing in the real world (e.g., simulations, crash tests). In the metaverse, some AI tools are going to be interesting for simulation purposes in the way they allow us to optimize what we are doing in the real world . . . And so, since I have new models of my cars, why not do the crash tests of my car in the metaverse? Because in that way I’m crashing virtual things instead of crashing real cars and wasting materials. This kind of thing is extreme, of course, because the regulator, in this case, will never accept that. So maybe the crash test in a virtual world, such as the metaverse, can be much more efficient in terms of energy, in terms of many things, and if the physics is right and the world is well modeled then the AI is very interesting there. (Luc Julia – Chief Scientific Officer, Renault Group) The metaverse and how it can be used will allow new ways of living or testing or simulating things in the real world, opening creative services. Companies need to carefully consider the processing power, electricity, and all the elements required to create these digital twins to guarantee that the metaverse may be physically correct and sustainable. There is a very interesting way the metaverse is being used today. We have a metaverse of the city of Seoul and a metaverse of the city of Seattle. This metaverse aims to give the ability to elderly to go to the city hall of the metaverse of Seattle, to renew their ID Cards or whatever, so to do something they cannot do because, in a huge city, it’s a pain for the old people to go from one place to another. So they can go into the metaverse because it is a replica of the city. In the known world that they are used to, they can navigate to the city hall, and go to do their stuff. So this is where

How AI can foster business creativity 79 the metaverse could be very interesting as a digital twin that is going to be so physically connected that I’m going to be able to navigate it the right way and then do what I cannot do anymore in the real world. (Luc Julia – Chief Scientific Officer, Renault Group)

Conclusions First, this chapter suggests that to obtain tangible benefits from AI-based tools to support organizational creativity, it is necessary to fulfill certain conditions: 1 Business creativity must be part of the entire company’s business strategy using a holistic approach. 2 AI, and the analysis or predictions it can produce, can provide relevant information about the business environment in which a company is evolving or wants to evolve, allowing it to build a richer and more holistic view. This knowledge can be the basis for creating new products, processes, and optimizations. Nevertheless, for this to work, creativity must be an integral part of the company’s strategy. 3 The company needs to be creative and proactive in using, managing, and acquiring relevant data sources and the tools that go with them. 4 Furthermore, the use of AI requires appropriate knowledge and skills. This has implications not only for the new educational paradigm requiring more hybrid competencies (STEM plus soft skills) for new job profiles but also for the need to design training for employees to familiarize users with the AI tools, their limits, and challenges. This knowledge sharing and training would allow employees to access a new and valuable source of information consisting of insights, analyses, and predictions that could enable them to act in a more data-driven/informed way. To foster business creativity and advance AI development, several actions need to be taken. This includes developing metrics, updating organizational measures, modifying governance structures, constructing new processes, rethinking job profiles, designing training programs, and imagining a new operating model. Leaders also need ways to understand not just the scope of the investment but also how improved creativity will translate into increased value – and how this increased value will be accounted for.

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Artificial Intelligence and creativity in marketing A proposed typology and new directions for academiaindustry collaborations Nisreen Ameen, Gagan Deep Sharma, and Shlomo Y. Tarba

Introduction Employers highlight creativity and emotional intelligence as two of the main skills required for professional success in today’s market and in that of the future (Hoffmann et al., 2020; Suh & Cho, 2020; Ameen et al., 2022a). Psychologists define creativity as the capacity to produce ideas that are both original and adaptive (Simonton, 2001). Previous studies have defined creativity as the generation of new ideas that are useful and implementable in the context of problem-solving, procedures, processes, and products (Amabile, 1983; Frare & Beuren, 2021). Employee creativity is a key factor in enabling the implementation of new ideas, which results in innovation (Amabile et al., 1996). In addition, the capacity to develop creative products is an important lever of firm profitability and sustainability (Bollinger, 2020). Due to globalization and to its associated high potential for the international marketing of products and services, firms are under constant pressure to be creative in order to be more visible and appealing to consumers worldwide. The impact of creativity and of the creative industries is felt not only at the local or regional economic levels but also at the global one (Gouvea & Vora, 2018). However, the human mind is unable to effectively analyze the already huge and still increasing volume of data currently available globally and to spot any correlations. To analyze and improve the customer journey, firms are increasingly relying on various cutting-edge technologies, including Artificial Intelligence (AI) (Ameen et al., 2021a). Huang and Rust (2018) defined AI as “machines that exhibit aspects of human intelligence”. In the field of computer science, Russell and Norvig (2009) described AI as the intelligence demonstrated by those computers that mimic human cognitive functions such as problem-solving. In consumer research, Longoni et al. (2019) defined AI as “any machine that uses any kind of algorithm or statistical model to perform perceptual, cognitive, and conversational functions typical of the human mind”. Ameen et al. DOI: 10.4324/9781003287582-8

Artificial Intelligence and creativity in marketing 83 (2022a, p. 1815) stated, “Machines classed as strong (super) AI would be indistinguishable from the human mind”. The authors explained that while we are currently relying on weak AI, which still has limited capabilities, strong AI is expected to be able to handle almost all skills required for creativity in the future. Weak (or narrow) AI, which includes all currently in use, is tailored to specific problems or tasks (Ameen et al., 2022a). Strong (or general) AI is defined as a machine with consciousness and mind that possesses intelligence in more than one specific area (Siau & Yang, 2017). By 2025, global investment in AI is expected to reach $232 billion (Schwandt, 2019), and companies are increasingly viewing the use of AI as a major factor in the future of marketing, including various creative areas (Ameen et al., 2021a; Ameen et al., 2021b). For example, the luxury vehicle brand Lexus produced the first filmed commercial written entirely by AI, having trained it with data pertaining to 15 years of award-winning luxury advertisements. Additionally, start-up companies such as Pencil are creating commericals using natural language processing as a form of AI (Khan & Mohiya, 2020). Nevertheless, the full potential of the use of AI in supporting business creativity and, indeed, its actual impact on employee creativity have not yet been fully investigated. As a result, the age-old understanding of creativity as being intuition-driven has not been updated to take the role of AI into account. Even though creativity is a core part of business success, the findings of the existing literature in respect to the role played by AI in this regard are fragmented and far from providing a clear direction for the further advancement of knowledge in this area. While earlier studies (e.g., Huang & Rust, 2020) have called for further research to be conducted on the potential of AI in relation to supporting creativity in various areas of business, a lack of synchronization remains in the work exhibiting the detailed pattern of research on AI and creativity in these areas. Hence, we approached this study with a view to consolidate the literature on this emerging field, understand the directions and status of such literature, and provide a road map suited to further advance the field and provide areas for future research, some of which could be based on collaborations between academics and practitioners. Our core question was not whether AI can outperform or replace humans in terms of creativity – as AI technology is still a tool (Nguyen et al., 2020) – rather, we questioned how AI technology, as a tool, can enhance human creativity when adopted by organizations and employees. Specifically, we proposed the following research questions: 1 Which skills are required to support employee creativity, how does AI interplay with human creativity, and how can AI successfully complement human creativity? 2 What are the current gaps and future directions of research on marketing creativity and AI that can be based on academia-industry collaborations?

84 Nisreen Ameen, Gagan Deep Sharma, and Shlomo Y. Tarba

Identification

Due to their wide acceptance – which ensures a high level of credibility – we retrieved the data for our study from both the Web of Science (WoS) and Scopus databases using keyword and Boolean criteria through advanced search. The data were then cleaned in line with our research objectives (Figure 5.1). The first step involved discarding any duplicates (107), which left us with 196 records. Furthermore, we screened these papers twice in regard to title and abstract and performed a full-text evaluation. In addition, we only kept those documents in which the searched keywords were present in the title, the abstract, and/or in the authors’ keywords, which resulted in 24 further articles being discarded. We then retained only those papers that were consistent with the “AI, creativity and marketing” literature and covered similar or identical subjects. This left us with a total of 156 documents, which we subsequently analyzed for the purpose of our study.

Records identified through WoS database searching (n = 142)

Additional records identified through Scopus database (n = 161)

Records screened (n = 196)

Records excluded based on titles and abstracts (n = 24)

Eligibility

Duplicate records removed (n = 107)

Full-text articles assessed for eligibility (n = 172)

Full text articles excluded as they were unrelated to AI, creativity and marketing literature (n = 16)

Included

Screening

Total records fetched using both databases (n = 303)

Studies included for bibliometric analysis (n = 156)

Figure 5.1 PRISMA study flow diagram

Artificial Intelligence and creativity in marketing 85 This chapter contributes to academic research and practice by providing insights into the impact of AI on business creativity through a review of the existing studies. More specifically, it presents a typology suited to help academics understand the current state of the impact of AI on the key skills required for creativity. In addition, it proposes directions for future research in the area of AI and business creativity. This chapter is structured as follows. Through a review of the existing literature, the next section provides an overview of the key skills required of employees for creativity in marketing from a psychological perspective. It then proposes a typology on the current state of the impact of AI on the key skills required for creativity. Finally, it offers new directions for future research and areas for academia-industry collaborations aimed at exploring how AI can be utilized to support creativity. Finally, it presents managerial implications for practitioners in this area.

AI and employee creativity from a psychological perspective Creativity has been associated with divergent thinking, which refers to the ability to engage in unconventional ways of reasoning, thus producing new ideas and solutions (Lucchiari et al., 2018). Previous studies have emphasized the link between psychology and creativity, studying, among other psychological aspects: innovativeness (Joy, 2005), intelligence (Burhan et al., 2017; Peters & Reveley, 2015), cognitive ability (Kellner et al., 2016), learning (Jou et al., 2010; Valaei et al., 2017), attention (Wilson et al., 2015), resilience (Hernández et al., 2015), memory (Wilson et al., 2015), emotions (Spendlove, 2007; Suveg & Zeman, 2011), intuition (Haag & Coget, 2010), thinking abilities (de Vere et al., 2010), intellect (Joy, 2005), perception (Taylor & Eisenman, 1964), ideas (Farid et al., 1993), and personality traits ((Guo et al., 2017). In the next sections, based on our review of the existing literature, we provide an overview of employee creativity at the individual, organizational, societal, and market levels. Employee creativity at the individual level At the individual level, creativity describes an individual’s abilities and traits linked to the generation of original ideas and problem-solving (Guilford, 1950; Ummar & Saleem, 2020). The individual level of creativity increases the likelihood of accepting novel product concepts without negatively affecting decision accuracy (Guenther et al., 2021). Furthermore, Ummar and Saleem (2020) found that creativity and innovativeness are parallel but nonidentical concepts. Creativity is predicted on originality and value, whereas innovativeness is predicted on commercial appeal along with originality and

86 Nisreen Ameen, Gagan Deep Sharma, and Shlomo Y. Tarba value in all products (Hoffmann et al., 2020; Ummar & Saleem, 2020). Héraud (2021) found that the most innovative managers are not necessarily those who have accumulated the most knowledge but those who are able to design new representations of the future and who know how to share their visions to enroll allies. However, creativity cannot be fully understood without considering the interplay of many territorially embedded factors (Héraud, 2021). As such, it is not limited to the individual level but extends to the societal one and can. Thus, be referred to as “collective creativity”, in which social capital plays an important role. Alves et al. (2021) identified various psychological traits related to individual creativity – including personality, intellect, temperament, physique, traits, habits, attitudes, value systems, self-concept, decision-making, and behavior. In addition, Frare and Beuren (2021) explained that psychological empowerment is a facilitator of individual creativity. Leon et al. (2019) found that the development of new creative ideas depends on the development of orderly relationships, or on “finding the thread that unites”. Knowledge is stored in the brain in distributed cortical networks, the immediate activation and connectivity of which often gives rise to the development of creative ideas (Petsche, 1996). The ability to develop unique relationships based on form or function is possibly the most important process in creative innovation (Heilman et al., 2003). Fischer et al. (2019) identified the three components of individual creativity: taking action due to enjoyment (intrinsic motivation), individual know-how and abilities (skills), and cognitive/perceptual styles and thinking skills (creativity-relevant processes). Over the years, academics have attempted to study the relationship between creativity and intelligence. For example, by studying the link between measures of intelligence and creativity, Kaufman et al. (2011) proposed a method aimed at extracting information on individuals’ divergent production and general creative ideas through the use of individually administered cognitive and accomplishment batteries. Hesmondhalgh and Baker (2010) relocated creativity to the terrain of peer-to-peer “interneting” as the definitive form of social – that is, non-market – production. The authors proposed an internet growth model that highlights the potential for digitally summoned communal intelligence in support of what Marxist philosopher George Caffentzis referred to as “post capitalist communication”. Squalli and Wilson (2014) conducted the first test of the intelligence–innovation hypothesis, which contributes to the debate on intelligence–creativity in psychology and innovation–growth in economics. The authors revealed that, having controlled for other variables, high-IQ states are more innovative and emphasized the importance of developing greater knowledge of the relationship between intellect and creative achievement. However, Altarriba and Avery (2021) and Desmet et al. (2021) recently found that there is no significant relationship between intelligence and creativity. Nevertheless, Altarriba and Avery (2021) did find a relationship between verbal fluency and creativity.

Artificial Intelligence and creativity in marketing 87 Spendlove (2008) proposed a triadic schema suited to identify emotions in a creative, product-oriented design and technology experience. The emotional dimension is recognized through three emerging domains: developing individuals’ emotional capacity to engage in a creative process (person); stimulating emotional engagement through appropriate learning contexts (process); and facilitating individuals’ emotional interaction with the outcomes of a creative process (person and process). Similarly, Alves et al. (2021) classified creativity in four different areas: person, process, product, and press. Previous research also found that age affects individual creativity levels. For example, Leon et al. (2019) found that younger adults are more capable than older ones to produce novel, original, appropriate, and creative ideas. Employee creativity at the organizational, societal, and market levels At the organizational, societal, and market levels, creative activities refer to the development of new ideas among teams, leading to innovation. For instance, Im et al. (2013) analyzed the effect of team dynamics on creativity and connected the latter to strategic innovation outcomes. The authors found that a firm’s ability to manage team dynamics in order to develop new innovative products and marketing strategies involves a dynamic capability suited to provide a competitive edge. In addition, a climate of creativity can be achieved by cultivating employee creativity, flexibility, and the availability of resources for innovation (Kim & Yoon, 2015). A culture that fosters innovation can significantly affect employee perceptions of innovation. Furthermore, situational job autonomy and momentary job engagement have been found to be daylevel predictors of innovative behaviors (Orth & Volmer, 2017). Limited attempts have hitherto been made to explore the potential of AI in aiding business creativity, particularly in the area of marketing (e.g., Vakratsas & Wang, 2020; Botega & da Silva, 2020). Thus, future research should be conducted to explore the potential of AI in aiding creativity in areas such as brand management, promotion, retailing, content marketing, and social media marketing. While some scholars have attempted to find ways to develop AIbased systems capable of generating new and novel ideas and evaluating their value (e.g., Nguyen et al., 2020; Vakratsas & Wang, 2021), future research could focus on finding new ways in which these systems can enhance human/ individual creativity, recognizing that an excessive reliance on AI can, in fact, undermine it. The marketing landscape is changing rapidly, and many studies have identified marketing automation as the way forward (e.g., Yang & Siau, 2018; Kumar et al., 2019; Davenport et al., 2020). However, employee creativity is still required. For example, content creation for commercials and creativity in chatbot conversation interaction still require human emotional intelligence. Hence, future research should focus on a balanced augmentation (combining human and AI capabilities), rather than only on automation.

88 Nisreen Ameen, Gagan Deep Sharma, and Shlomo Y. Tarba This would also help to preserve employee confidence in their own creativity and further improve it. Future research could focus on how firms can develop their organizational culture in order to promote creativity among their employees in the area of marketing on the basis of augmentation – as opposed to automation. We would also recommend a revisitation of those contexts – in which employee creativity is applied – that may require a shift in or a new set of creativity skills and the associated psychological aspects. For example, if the employees who work in those areas of marketing that make use of AI are to be effectively creative, they will need to develop a sufficient level of understanding of how AI technology works and of the issues customers encounter when interacting with it – such as a lack of empathy, emotions, and problem-solving skills and the inability to handle unique situations such as service recovery. These skills could be just as important as the core elements of creativity – novelty, originality, and appropriateness (Hoffmann et al., 2020; Ummar & Saleem, 2020). We would, therefore, encourage future research to combine an assessment of AI capabilities and of the new skills required by employees for creativity with the existing theories in the area of business creativity. Creativity in the business landscape is expected to be redefined by the changes taking place in it. For example, the current applications of AI in marketing – such as conversational agents (chatbots), AI algorithms in advertising, and semantic marketing – present opportunities for the application of human creativity to improve the functionality of AI in these areas in order to provide a better and more enjoyable experience at different touchpoints of the customer journey (Ameen et al., 2022b). In essence, human creativity would greatly benefit areas such as content marketing, social listening, chatbots, and digital assistant conversational content. Future studies could, thus, focus on developing a system that combines human and AI capabilities in order to measure creativity in marketing.

A proposed typology of skills required for business creativity and AI AI can act as a useful tool suited to support business creativity through its ability to handle complex and large amounts of data, memory, and learning. In these areas, creativity can be enhanced by the careful combination and balancing of human and AI capabilities. Based on our earlier review of the existing literature on the skills required for employee creativity in marketing and AI (Ameen et al., 2022a), we provide an overview of the key skills required for business creativity – which were identified in the studies outlined in the earlier section (Figure 5.2) – and of the impact AI has on them, which, in some cases, is strong. Our proposed typology can assist both academics and practitioners in better understanding the key skills required for creativity and the current

Artificial Intelligence and creativity in marketing 89

Emotional skills

Key skills required for business creativity and the impact of artificial intelligence* Emotional intelligence

Empathy

Emotions

Social skills

Congnitive skills

Learning Cognitive ability

Reasoning

Idea generation Persuasion

Problem solving

Decision making Memory

Low

Intelligence Processing massive amounts of data High

Current impact of artificial intelligence *As found in the systematic literatur review conducted by Ameen et al (2022a)

Figure 5.2 The impact of AI on the key skills required for business creativity

impact AI has on these skills and areas. However, we expect this impact to change as the technology evolves further and we move toward strong AI. AI, as a technological tool, is still unable to fully handle some important areas of creativity that emerge from human psychology – such as emotional intelligence, intuition, assessing the performance of creativity, problemsolving, and finding the threads that unite different relationships. As such, it cannot replace humans and should not be heavily relied upon. A balanced combination of human and AI abilities can yield the best results in terms of creativity. As AI is better suited for repetitive tasks and as creativity, in most cases, is based on generating ideas without prior knowledge, the human factor is still required. Furthermore, idea generation, which is part of the psychology of human creativity, involves initiating ideas and using emotional intelligence when solving problems that emerge from listening to and interacting with customers, clients, and stakeholders, which is not the case with AI.

Directions for future research The development of quantum computing and generative AI marks a leap forward in processing capability, with massive performance increases for specific usages. Quantum computing is a new generation technology that involves processor speeds 158 million times higher than those of the most sophisticated supercomputers available in the world today (Smith, 2022). Companies are aware of the potential impact of this technology. For example, Goldman Sachs announced that they expect to be able to introduce quantum algorithms to price financial

90 Nisreen Ameen, Gagan Deep Sharma, and Shlomo Y. Tarba instruments in as little as five years (Bova et al., 2021). In any case, we expect this technology to also have a significant impact on employee creativity in the future. Quantum computers are fast, are capable of solving extremely complex problems and running complex simulations, and offer quantum-assisted software suited to unlock new areas of creativity (Etim, 2021). This can have a significant impact on employee creativity and on shaping the future of business creativity. We would, thus, encourage future researchers to explore this area. Table 5.1 provides a summary of our suggested directions for future research. We would encourage academics and practitioners to collaborate on impactful research aimed at addressing the main areas identified in our work. This would also open new opportunities for funding and collaborating with government bodies such as the European Commission and UK Research and Innovation. Table 5.1 Key avenues for academia-industry collaborations in future research on AI and marketing creativity Directions for future research

Key future research areas

Potential topics

• Exploring the impact of quantum computing on employee creativity • Finding the right balance to be struck between human and AI capabilities and the conditions required to utilize the power of AI without hindering employee creativity • Exploring the potential of AI in aiding creativity in areas such as brand management, promotion, retail, content marketing, and social media marketing • Finding new ways in which AI-based systems can enhance human/individual creativity, recognizing that excessive reliance on AI can, in fact, undermine it • Implementing a balanced augmentation (combining human and AI capabilities) instead of automation • Exploring how firms can shift their organizational culture to promote creativity among employees on the basis of augmentation – and understanding its impact on employee psychology – and promoting creativity among individuals • Revisiting those contexts in which human creativity is applied and that may require a shift in or a new set of creativity skills and identifying the associated psychological aspects • Developing a sufficient level of understanding of how AI technology works and the types of problems customers encounter when interacting with it • Exploring how human creativity can assist in improving content marketing, social listening, chatbots, and digital assistants’ conversational content • Developing a system to measure creativity performance that is capable of encompassing the new areas of AI-assisted digital marketing – such as digital assistants, chatbot services, social media content, and social listening (Continued)

Artificial Intelligence and creativity in marketing 91 Table 5.1 (Continued) Directions for future research

Key future research areas

New methods

• Conducting in-depth qualitative research aimed at collecting data from employees in creative roles in order to understand their perceptions of the shifts in business creativity brought about by the emergence of AI and how these have affected the ways in which they do their jobs • Conducting multi-study and multi-source data research aimed at gaining a comprehensive understanding of the application of AI in business creativity and of its impact on individual creativity • Collaborations between academia and industry to yield the best results stemming from real-world situations and settings • Conducting longitudinal research based on panel data analysis to study the changes occurring in employee creativity over time in the various areas which AI is applied • Utilizing big data analytics methods to identify the points in the digital customer journey that require higher levels of creativity or at which creativity can make significant improvements to the entire journey • Conducting cross-national and cross-cultural studies aimed at identifying any similarities and differences in the skills required for creativity • Collecting data from consumers in both developed and developing countries in order to identify those parts of the customer journey in which creativity can have a particularly significant positive impact

New contexts

As outlined in the previous sections of this chapter, researcher attention has only recently started to shift toward studying AI through the lens of creativity. Hence, the theoretical development of the field is currently in its nascent stage. Wu et al. (2022) identified seven important theories in the information systems domain – namely, transactive memory system, impression management theory, flow theory, structural holes theory, resource dependence theory, social presence theory, and illusion of control. However, the human creativity context of these as well as other theories remains underexplored, while the theories focusing on human creativity – for example, affordance theory (Gibson, 1977) and material engagement theory (Aydin et al., 2019; Malafouris, 2013) – have not been applied to the area of AI adoption and creativity. Table 5.1 shows that contextualizing the theories from these two related branches with each other shows promise in relation to helping evolve an integrated perspective suited to leverage the creative value of human-machine integration. In order to fully utilize AI without impeding or sacrificing employee creativity and innovation, a balance needs to be struck between human and AI skills. Future research could thus explore the numerous domains and sub-domains listed in Table 5.1 in relation to business policy and practice,

92 Nisreen Ameen, Gagan Deep Sharma, and Shlomo Y. Tarba organizational culture, business performance, etc. Presently varied research approaches to business/organizational issues are being taken. Consequently, it would be essential to apply qualitative, quantitative, longitudinal, and mixed methodologies to address the aforementioned issues of merging creativity with AI. We envisage that these theories, topics, and methods would enable the simultaneous handling of global, national, and local business challenges in a manner that would see AI wielding a strong beneficial influence on human creativity and business innovation. To maximize the impact of the related findings, we would recommend that these issues be tackled by teams combining academics and practitioners. Finally, there is a lack of research conducted within the context of developing countries, in which AI is also increasingly being used in different business areas but with potentially different impacts of culture on creativity. Thus, future studies could involve cross-national and cross-cultural research aimed at identifying any similarities and differences in the skills required for business creativity and the impact of AI on these skills. Such studies could reveal the true impact of culture on employee creativity skills and on the ways in which AI can be utilized.

Managerial implications As advanced digital technologies are rapidly changing the business landscape, firms should reconsider how and where they should apply creativity, recognizing that it may very well extend beyond the context of advertising and product and service design. Although most modern business organizations view AI as a critical tool suited to attain competitive advantage (Ransbotham et al., 2017), they struggle to use it effectively in the pursuit for corporate excellence (Fountaine et al., 2019). Firms should identify the areas (touchpoints) in the customer journey that would benefit the most from creativity – for example, content marketing, social listening, chatbots, and digital assistant conversational content. The type and level of creativity needed in these areas may differ greatly from that related to traditional marketing methods. Hence, the new skills required for creativity among employees may also be different. For example, companies could focus on the use of AI-assisted creativity to augment employee empathy, verbal communication, emotional intelligence skills, and ability to handle new and complex situations. Employee attitudes toward their workplaces may shift as a result of automation as they turn their skills and attention to other, perhaps more fulfilling, endeavors. One of the greatest concerns posed by the onslaught of newer technologies (including AI) is that it may have a negative influence on the creativity of business stakeholders, consequently also affecting business creativity (Gobet & Sala, 2019). Businesses can overcome these barriers by adopting integrative,

Artificial Intelligence and creativity in marketing 93 rather than isolative, strategies. In relation to the manufacturing of new products, for instance, AI-based ideas may be of use to creators, particularly if they include design aid, which might stimulate creativity. For instance, AI may enable advertising and marketing teams to save significant amounts of money, time, and effort. The launching of fresh campaigns would require little time, as businesses would be able to rapidly react to changing situations, address breaking news or trends, and proactively develop new products and services through the use of AI-based innovation. Using AI, any material and resources could also be readily replicated in several languages, enabling businesses to quickly reach global audiences effortlessly and affordably. For such reasons, it would be crucial to invest in AI for innovative applications (Fountaine et al., 2019). Furthermore, the adoption of AI in business would require companies to maintain good equipment levels in regard to both the latest technology or tools and skilled employees. Training and hiring creative individuals that are knowledgeable in AI, machine learning, and deep learning would represent a great leap forward for the corporate world. Furthermore, businesses would need to ensure that any prospective employees possess the willingness and inquisitiveness needed to push the envelope, as human intuitive reasoning is increasingly combined with the accuracy and precision of machines. The intersection of AI with creativity would help to improve business performance in operational, financial, market, and sustainability terms (Enholm et al., 2022). Businesses would, however, need to nurture the creativity of their various stakeholders, integrate it with AI, and embed these in the business model.

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6

Toward AI-enabled support for creative thinking about business models Mark Dowsett, Neil Maiden, and Charles Baden-Fuller

Creativity, business models, and business strategies Creativity is defined as the ability to produce work that is novel and original as well as appropriate and useful (Sternberg, 1999). The need for more creative thinking to solve complex problems is well-documented (e.g., Isaksen et al., 2011). These problems can be diverse. Creative problem-solving has been applied many times to solve complex problems, from enabling people to work from home effectively during the pandemic (e.g., Weigelt et. 2021) to enabling the crew of the Apollo 13 spacecraft to return to earth safely (e.g., King, 2011). Unsurprisingly, creativity is also increasingly sought after to solve business problems. The World Economic Forum identifies creativity and complex problem-solving as two essential and related skills, and research by Nesta revealed that creativity is consistently identified as the most significant predictor for the likelihood of occupation growth between now and 2030 (Easton & Djumalieva, 2018). Upfront creative thinking is also an essential prerequisite for downstream innovation, to generate the ideas with which to design and develop business innovations (Design Council, 2011). Business problems have become significantly more complex and challenging in the digital age, in part because of the changes to where creativity is needed. Through the 19th and 20th centuries, fortunes were made by businesses becoming more creative about their services and delivery though the distribution chain. New products, processes, and forms of wholesaling and retailing have been strongly evidenced. But in most cases, engagement with customers did not change. Customers were offered a predesigned product or service but little choice about how to engage with the firm. By contrast, from the start of this millennium, new firms put out novel and valued offerings that engaged with customers differently in both the digital and physical spaces. These offerings were often Big-C creative outcomes, defined as eminent and relatively rare contributions to society based on Kaufman and Beghetto’s (2009) distinction between different degrees of creative outcome. Thus, we have advertising-supported search engines (such as Google), DOI: 10.4324/9781003287582-9

100 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller app-enabled mobile phones (such as Apple), and novel forms of entertainment (such as streaming from Spotify), ways of shopping (such as Amazon), and ways of communicating (such as WhatsApp). And in the B2B world, we have novel forms of engagement such as offering capital goods on a “needs basis” with guaranteed performance (such as Rolls Royce’s Power by the Hour). All of these firms shared something – they engaged with customers in a novel manner. The challenge of being creative in business has become significantly more complex as a result of these changes. Firms need to think beyond creative products and processes to how to engage with customers and linkages that might exist between novel forms of customer engagement and novel products, services, and processes. Often these forms are outcomes of Pro-C creativity that exhibit professional-level expertise for earning a living (Kaufman & Beghetto, 2009), such as a new customer onboarding service and more innovative forms of customer engagement to co-develop new products. Generating these forms of creative outcome is often enabled by Mini-C creativity, a different form of outcome, which are novel and personally meaningful interpretations of a peoples’ own experiences, such as learning about new forms of customer engagement in other cultures. It is also enabled by Little-c creativity that leads to the generation of novel everyday outcomes, such as new insights about engaging your customers from competitor practices. The term used to describe how value is created and captured in a holistic sense using events at the customer-firm boundary is the business model. Typical firms face significant challenges in thinking about their next steps and strategies to deploy resources to achieve favorable outcomes. They always have to keep a watch on whether the current, seemingly tactical, minor creative challenge is not a signal for a major shift in business model approach. However, in spite of these recognized trends and opportunities, creative thinking applied to business models to generate Pro-C, Mini-C and Little-c outcomes has received little attention from the majority of businesses and academics writing about business. We claim that this is a missed opportunity. One reason for this missed opportunity is that many businesses lack sufficient creativity knowledge. In this chapter, we define creativity knowledge as operational knowledge about the frameworks, processes, techniques, and tools with which to think creatively, systematically and regularly, as well the established practices for applying this knowledge in different contexts. Amabile and Pratt (2016) assumed three major components necessary for creativity in any domain: expertise, intrinsic task motivation, and creative thinking skill. We observed that most business leaders have work expertise and intrinsic motivation to develop new business models and strategies but most lack the creativity knowledge needed for effective and regular problem-solving. Traditional means of introducing this knowledge (e.g., with training or expert facilitation) have not been effective. There are multiple possible reasons for this and include the inaccessibility of knowledge about how to be creative in academic papers, textbooks, and websites; insufficient time to organize facilitated activities such as workshops; and lack of committed management support.

AI-enabled support for creative thinking about business models 101 New digital technologies provide one alternative means of making this creativity knowledge available to business leaders. Artificial Intelligence technologies that reason automatically using codified creativity knowledge have been demonstrated to support human creative thinking in different professional domains (e.g., Maiden et al., 2020a; 2020b). This codification translates operational knowledge about creative thinking frameworks, processes, techniques, and tools into machine-readable forms that are implemented in software as, for example, algorithms, rules, and interactive user guidance. The technologies implemented with this knowledge are one form of human-centered Artificial Intelligence (HCAI), which aspires to empower humans rather than automate human work (Xu, 2019; Yang et al., 2020), albeit with machine reasoning designed to generate support for human reasoning. Ben Shneiderman argues that HCAI needs to reframe AI to be “in-the-loop” around humans to support people’s self-efficacy, creativity, and social participation (Shneiderman, 2021). To direct the development of new digital tools with humans at their center, he offered what he called three fresh ideas: (a) to deliver high levels of human control as well as automation, (b) to design to empower people with powerful tool-like appliances rather than emulate human expertise, and (c) to promote a governance structure that describes how to develop more reliable systems and maintain a safety culture. Modern smartphone cameras, thermostats, elevators, and dishwashers are new tools with AI capabilities that implement these ideas (Shneiderman, 2020). Therefore, in this chapter, we report ongoing research that codified creativity knowledge in a new digital prototype that delivered a form of humancentered Artificial Intelligence to support human creative thinking about business models and strategies. We summarize recent developments in HCAI and digital technologies to augment human creativity and different forms of creativity knowledge available to be codified to support creative thinking. We then introduce the concepts of business models and strategies and explicate the Business Model Zoo as one source of information about models to manipulate automatically with Artificial Intelligence algorithms. After these reviews, we report on our development of a novel prototype, called the Business Opportunity Builder, which uses AI algorithms to encourage creative thinking about business models and strategies. We also report our engagements with senior business professionals who walked through and provided feedback on the potential of this prototype. The chapter ends with an outline of the steps needed to deliver effective AI-enabled support for creative thinking about business models to businesses.

Human-centered Artificial Intelligence There is a growing body of work that is seeking to understand HCAI. Multiple authors (e.g., Shneiderman, 2020) and institutions (e.g., Stanford, 2022) agree on the aims of HCAI to empower rather than replace humans (e.g., using conversational agents that support communities) (Wang et al., 2021). However, so

102 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller far, there has been little progress to operationalize Shneiderman’s (2020) three key ideas to deliver more effective HCAI. Instead, most reports have focused on the need to develop user-centered processes to design AI systems that learn and evolve. For example, Yang et al. (2020) investigated why systems that learn and evolve are more difficult than conventional ones to design by mapping different human-AI interaction design challenges onto user-centered design processes. Similarly, Xu (2019) proposed a HCAI framework for developing more effective AI tools based on new challenges for usable and useful systems, which Olsson and Väänänen (2021) extended with their 4P (product, people, principles, and process) model of AI design to describe the expected dynamics in UX design practices as a baseline for new design processes. Like most of this reported work, Xu’s (2019) framework assumed that AI tools use black-box machine learning systems with neural networks for pattern recognition in deep learning systems that require capabilities to explain to users the reasons for their outputs. Indeed, Yang et al. (2020) highlighted an absence of a common definition of AI from the research discourse around human-AI interaction. Their review revealed a range of poorly defined terms, such as machine learning, intelligent, and AI-infused systems, which led them to propose an AI design complexity map defining four levels of AI systems. According to this map, simple probabilistic systems at Level 1 exploit selfcontained datasets to produce a small, fixed set of outputs, whereas evolving adaptive systems at Level 4 learn from new data even after deployment, to produce adaptive, open-ended outputs that resist abstraction. All four of these levels assumed the use of black-box machine learning algorithms that need to explain their outcomes to end users– an approach that so far has met with only limited success (e.g., Došilović et al., 2019). By contrast, other types of AI system that can deliver explanations to users (e.g., rule-based expert systems) continue to be effective and deliver valuable outcomes in domains such as medical billing (Abdullah et al., 2017) and e-government (Hossain et al., 2015). As we will report, knowledge about creative thinking processes and techniques can be codified as generative rules. Likewise, different variations of case-based reasoning systems (Kolodner, 1993) have also been effective for problem-solving in domains such as healthcare (e.g., Bichindaritza & Marling, 2006) and law (e.g., Rissland, 2005). Indeed, the importance of case studies in business thinking and education reveals the potential value of reasoning across exemplars. AI systems that implement rule- and case-based reasoning can be treated as within the remit of HCAI and co-creative AI tools and enable the more humancentered approaches required for more explainable AI, as outlined in Ehsan et al. (2021). Therefore, based on these previous research outcomes, we sought to develop an alternative HCAI prototype that automatically manipulated codified creativity knowledge and available digital information about types of business models.

AI-enabled support for creative thinking about business models 103

Digital creativity support and co-creative AI tools Digital creativity support tools help people to engage creatively with the world (e.g., Cherry & LaTulipe, 2014) and have been the subject of considerable research and development. Most have supported the generation of Pro-C and Little-c creative outcomes (Kauffman & Beghetto, 2009) and used different forms of interaction to help users be more creative. For example, the combinFormation system searched web information to support a user’s creative thinking (Kerne et al., 2008) and the Carer app searched cases of good practices to encourage carers to ideate about the care for older people with dementia (Zachos et al., 2013). Both the Dynamic HomeFinder (Williamson & Shneiderman, 1992) and a digital tabletop for making biological discoveries (Wu et al., (2011) used interactive visualizations to support creative thinking. Tools that support collaborative creativity have been implemented in many sectors including education (Aragon et al., 2009), television production (Bartindale et al., 2013), and real-time design work with crowds (Andolina et al., 2017). And other tools have appropriated different digital technologies to encourage creative thinking (e.g., the Trigger Shift tool for performance art in theatre; Honauer & Hornecker, 2015). However, although some have been demonstrated to augment human creative thinking, fewer digital creativity support tools have been implemented to support work in non-creative professional domains such as business. One exception was the Risk Hunting app, which supported creative thinking to resolve health-and-safety risks in manufacturing some codified knowledge of selected TRIZ principles (Maiden et al., 2020a). Another was JECT.AI, which integrated knowledge of creative strategies used by experienced journalists with information from tens of millions of published news and scientific stories to support journalists to generate more creative angles for new stories (Maiden et al., 2020b). And Sport Sparks codified knowledge of creative thinking techniques such as constraint removal to guide coaches to generate novel and useful action plans to overcome athlete challenges (Maiden et al., 2021). Moreover, use of this knowledge codification in the JECT.AI and Sport Sparks tools was demonstrated to contribute to the creative thinking of professional journalists (Maiden et al., 2020b) and sports coaches (Maiden et al., 2022). Moreover, more recent digital tools to support human creative thinking deploy different forms of machine reasoning and are often referred to as co-creative AI tools (e.g., Long et al., 2021). These tools can be framed as examples of “humans-in-the-loop” around Artificial Intelligence systems (Shneiderman, 2020). The research focus is to design machine intelligence, rather than to augment human behavior. For example, Calliope deployed generative design algorithms to search large possible design spaces (Davis, 2021), Shelley implemented deep learning algorithms to generate horror stories (Yanardag et al., 2021), and a computational model encouraged conceptual shifts based on clustering of deep features from a database of sketches (Karimi et al., 2019). Unsurprisingly, reports

104 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller of these tools have highlighted the capabilities of the machine reasoning and made little reference to support for creative thinking by end users. However, these reports do reveal the potential of machine reasoning to manipulate knowledge and information to generate outcomes that can guide and inform human creative thinking. Returning to Amabile and Pratt’s (2016) three required components for creative thinking in any domain, we assert that machine reasoning about codified creativity knowledge has the potential to substitute for at least some of the creativity knowledge lacking in many business users. To explore this possibility, selected knowledge about creative problem-solving was codified to be manipulated automatically in a new digital prototype that was populated with information about business models and strategies. The two next sections explore the forms of creativity knowledge that were available to be codified and the digital information about business models and strategies about which to reason automatically.

Knowledge about creative processes and techniques Creative problem-solving processes can be described as iterations of divergent and convergent thinking. The divergent thinking is intended to manipulate information to generate many possibilities and the convergent thinking to generate fewer, more complete ones (e.g., Plsek, 1997). Within this framing, Boden (1990) distinguished between two core types of creativity – exploratory and transformational. Exploratory creativity assumes a defined space of partial and complete possibilities to explore – a space that also implies the existence of rules that define the space. Changes to these rules produce what might be thought of as a paradigm shift, called transformational creativity (Boden, 1990). Ideas that are novel and useful are reached in the space by a set of generative rules for divergent thinking and convergent thinking. Boden also identified a third form – a specific form of exploratory creativity that she called combinational creativity, which is the process of making unfamiliar connections between familiar items in the predefined search space using a different set of generative rules (Boden, 1990). These three types of creativity can provide a valuable framing of not only how techniques manipulate knowledge during creative problem-solving but also how this knowledge is codified for manipulation by algorithms. Many creative thinking techniques developed to manipulate problem and solution information have been published. Collections of these techniques are reported widely in academic papers (e.g., Tauber, 1972), books (e.g., Michalko, 2006), and websites (e.g., Mycoted, 2022). We have observed that most of the techniques support one of Boden’s three types of creativity and provide generative rules that users can reason with to generate new ideas. For example, the constraint removal (Onarheim, 2012) and assumption busting (Michalko, 2006) techniques direct their users to challenge the constraints and assumptions related to a problem and, hence, change the

AI-enabled support for creative thinking about business models 105 rules that frame a space of possibilities to support transformational creativity. The TRIZ inventive principles (Altshuller, 1999) and creativity triggers technique (Giunta et al. 2022) direct users to discover possibilities in a space that have qualities associated historically with more creative outcomes – qualities such as asymmetry (Altshuller, 1999) and playfulness (Burnay et al., 2016). Each quality can be translated into one or more generative rules with which to discover possibilities and support exploratory creativity. And techniques such as storyboards (e.g., Stickdorn & Schneider, 2010) and heuristic ideation (e.g., Tauber, 1972) use the timelines of stories and combination matrices to implement rules with which to make unfamiliar connections between familiar items and support combinational creativity. These observations led us to make an important claim – that the manipulation of rules that frame spaces and/or discover possibilities in them is open to automation with algorithms to the benefit of businesses. As well as making this creativity knowledge more accessible to business users, algorithms that can manipulate that knowledge when codified can generate large numbers of possible ideas quickly, thereby increasing idea quantity. Furthermore, the algorithms can reason with information and data not available to individuals to increase idea quality. And tools accessible via workplace desktops or smart mobile devices can integrate creative thinking more effectively into existing work processes.

Information about business strategies and models Strategy has been defined in many ways, but almost all definitions share a common core – how a firm deploys its resources to achieve a favorable outcome (Grant, 2021). The focus in this chapter is on strategies of single business firms and units of larger firms rather than larger corporations that encompass multiple divisions. Key factors that determine the success of a firm’s strategy is how that firm designs and makes its core offer, puts that offer into the market to attract customers, and makes customers pay. It is about how value is created in the eyes of its customers then captured in the form of revenues (Teece, 2010; Baden-Fuller & Morgan, 2010). There are, of course, other matters of concern to managers in the firm that are also strategic, such as labor contracts, supply chain contracts, and firm organization, but these typically are subsidiary to the wider question: what are we offering, to whom, and how are we asking them to pay for the offer? The arrival of the digital age has meant that concerns that were traditionally separated have come together. In the past, most firms were concerned with how their offer was designed and produced. The challenge of selling was typically seen as important but tactical and soluble if the other challenges were solved. Today, this separation can no longer be justified. How firms engage with customers at the customer-firm boundary is no longer a matter of

106 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller tactical concern – it is strategic. Moreover, the choice of what happens on the customer-firm boundary influences the whole firm. What was a previously a modular or separable concern has now become integrated or systemic. Much of the existing literature on business models focuses on this important connection between the core processes of the firm and activities along the boundary between the customer and the firm (Teece, 2010; Baden-Fuller & Haelfiger, 2013). The Business Model Zoo (BMZ) initiative was developed to present different forms of this connection to businesses. It set out four key choices, each of which represented a different way by which these connections are made: product, solutions, matchmaking, and mediated multi-sided. In the product business model type, the boundary decision can be separated from the core processes of the firm because the relationship between the firm and its customer is transactional (Baden-Fuller & Haelfiger, 2013; BadenFuller et al., 2017). This means that creative concerns about what happens inside the firm regarding the design of the offer and its production can be completely separated from distribution, marketing, and selling. In the solutions business model type, they cannot be so arranged – how the firm sells critically influences the design of the offer and vice-versa. The digital streaming platform Spotify exemplifies these connections – the method of streaming of the content deeply influences the viewers experience, and its ability to suggest the right movie to watch and remember when you stopped watching is as important to the enjoyment as the library choices. Likewise, the physical experience of the ambiance for the delivery of the high-quality haircut or shave is as important as the cut itself. In these latter two cases, it is clear that all firm decisions have to look holistically at what is going on in all parts of the value chain, including the value capture. The other two business model types replicate the first two situations but for firms that have multiple customer groups. Are these firms running the different groups separately? This is the case for the sheep farmer who sells wool and meat without being concerned about the interactions between the buyers and the case of the matchmaker who has a transactional relationship with the buyers and sellers. This is also the case for firms confronted by interactions between the two sides, for example, not only for the platform gaming firm that is selling advertisements embedded within the games but also for the traditional charity helping the poor by endeavoring to engage its two sides – donors and beneficiaries – to interact to stimulate more giving and ensure the beneficiaries recognize the quality of the core charity offerings. A further layer of complexity is to consider if the relationship between the firm and each of its sides or customer groups should be transactional (as with Amazon and its relationships with sellers located in China) or relational (as with Google and its advertisers, who are deeply tied into the Google machine through complex incentives). The BMZ initiative launched its website as a vehicle to bring this view of different business model types to life. It contains descriptions of the four types and exemplars to further understanding with real examples. Each type

AI-enabled support for creative thinking about business models 107 01. PRODUCT MODEL The company develops a product or standardised service and sells it to customers. The value proposition is transactional: to provide a product or standardised service that consumers will buy

02. SOLUTIONS MODEL

03. MATCHMAKING MODEL

The company engages with a customer about a problem the customer faces, and provides an integrated solution. The value proposition is relational: to tailor solutions to each customer.

The company joins buyers and sellers in its online or physical marketplace. The value proposition is transactional: to facilitate exchange.

04. MULTI-SIDED MODEL The company provides different products or services to different customer groups. The value proposition is multi-sided: one customer group gets additional benefits from the other group's transactions

Figure 6.1 The four model types available via the Business Model Zoo (BMZ)

that was discoverable in the website are summarized in Figure 6.1. Information about them and their related strategies provided the baseline input for the planned automated reasoning.

BOB – an interactive prototype for creative thinking about business models Next, to explore how to reason automatically about codified creativity knowledge and information about business models, we designed, implemented, and evaluated a simple digital prototype. The prototype was called the Business Opportunity Builder, or BOB for short. The development of BOB followed an established design science approach – one that sought to design and investigate artifacts that interact in and with a problem context to improve something in that context (Wieringa, 2014). We designed a new version of an artifact to interact with BOB that were then analyzed in the context of use with business users to investigate whether it had the potential to support their creative thinking and generate Pro-C, Mini-C and Little-c creative outcomes. The authors used co-design techniques to develop the prototype. The lead author collaboratively developed with business users a series of simple wireframes of key interactions. These wireframes were then used to develop a small number of partial prototypes that were shared with other business users to collect their feedback on the designed content, guidance, and user journey. Details of this co-design process are reported in Dowsett (2020).

Full version of the BOB prototype The first full version of the interactive BOB prototype was designed to support business professionals to think creatively about their business models and strategies using different co-designed features. It was developed as an

108 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller interactive web application accessible via a URL to enable users to provide feedback on it without the need to download or log in to software. Each of the prototype’s key features and, where needed, their underpinning algorithms are reported in turn.

The challenge description The prototype offered the user a simple one-line text entry feature with which to enter a current business challenge to investigate (see Figure 6.2). To guide users, the prototype also listed examples of challenges that could be explored as a guide for what to enter.

The business model finder and descriptions Based on the design of the earlier Business Model Zoo website, a simple online wizard guided a user through a series of four questions, the answers to two of which enabled the prototype to present one of the four selected business model types to the user. The wizard was designed to be simple and quick to use. It elicited each response on a 1 to 4 scale. Users simply clicked one option on each scale – no text entry was needed. Examples of two of the four questions asked by the wizard are shown in Figure 6.3. The first question asked the user to select the extent to which customers of the business pay for physical or digital products and services. The second asked the user to select the extent to which the business customizes its product or service to each customer’s needs. Users were able to navigate forwards and backwards through the wizard to change their answers to one or more questions before requesting the business model type selected by the prototype. The prototype implemented a simple algorithm to select one business model type automatically in response to each four responses. The inputs to the algorithm were only the four response values entered by the user, and the output was one of the four predefined business model types. The algorithm

Figure 6.2 The prototype’s feature for entering a description of a business challenge

AI-enabled support for creative thinking about business models 109

Figure 6.3 Two questions asked by the business model finder to select the relevant business model type

was designed to select one and only one model type in response to all possible combinations of the input values. The selected business model type was described using a short text description and single graphic taken from the original BMZ website. It was presented to enable users to review and confirm each selected model before progressing. One example of this text and graphic is shown in Figure 6.4. If needed, the user was able to return to the wizard

110 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller

Figure 6.4 A summary description of one business model type presented to users

questions to change answers and restart the process. If the user agreed with the selected business model type, the prototype provided different features with which to explore that model type. First, the prototype presented a more detailed description of the selected business model type in text form. One section of the prototype’s description of the multi-sided model type is shown in Figure 6.5. This description was supported by a short animation video that also described the model type. The video included a talking lion taken from the original Business Model Zoo website. The description was supplemented with links to case studies – curated examples of businesses that had implemented the presented type of business model. The names of these case studies are shown to the right of the model text, see Figure 6.5.

The business model examples Each retrieved business model type had been curated and associated with multiple case studies of businesses identified to have implemented that type. Each example represented one case. A total of 106 case studies had been developed in the original Business Model Zoo project, and all were implemented in the new prototype. A user was able to review all of the cases linked to the selected business model type. Each case study was described in text form with a header introducing the business and fit to the model type and an overview of the business and its activities, history, and customers. An example description in the prototype for the Waze case study is depicted in Figure 6.6. As such, the

AI-enabled support for creative thinking about business models 111

Figure 6.5 A more complete description of one business model type and, on the right, the titles of curated case studies associated to that type

prototype encouraged users to engage in simple case-based reasoning – reasoning with previous experiences to understand and solve new problems in similar ways (Kolodner, 1993). However, the users were not able to interact directly with each description, so the prototype only provided the baseline resources with which to undertake case-based reasoning. No interactive support was offered. Nonetheless, user access to these descriptions was expected to support them to generate at least Pro-C and Little-c creative outcomes. Each case could offer one draft Pro-C solution to a business modeling–related problem, and smaller elements of that case could direct thinking to generate Little-c outcomes that others might not consider novel but could contribute to future Pro-C solutions. By contrast, to support more directed creative thinking with these case studies, the prototype also supported users to interact with them and explore possibilities that might be judged to be more novel and useful. First, a carousel presented three case studies at a time on interactive cards, so that the user could explore the cases quickly by scrolling backwards and forwards and filtering the case studies that were presented (see Figure 6.7). In turn, each interactive card was divided into three parts – the name and summary of the case study, an option to view the full case study description

112 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller

Figure 6.6 One case study description retrieved and presented by the prototype to users

Figure 6.7 Multiple interactive case studies presented in the prototype’s carousel

AI-enabled support for creative thinking about business models 113

Figure 6.8 An interaction with case study theme presented in the carousel to encourage exploratory creative thinking

shown in Figure 6.7, and interactive themes automatically generated by the prototype for each case. The prototype had the potential to generate the interactive themes automatically for each case using entity extraction algorithms, although the first version presented manually generated themes. The user could then click on each theme to interrogate it and to discover more information related directly and indirectly to the case. An example of this interrogation is shown in Figure 6.8. The user has selected the Credit Karma case and clicked on the theme “credit scoring”. In response, the prototype has presented an interactive pop-up populated automatically with links to online documents discovered using Google and other web searches generated automatically from simple creative searches of online content. These pop-ups and links were designed to encourage users to explore multiple possibilities for new ideas, consistent with Boden’s definition of exploratory creativity, and generate Pro-C, Mini-C and Little-c creative outcomes. The queries automatically generated by the prototype acted as rules to guide users to discover more ideas possibilities in the spaces.

Insights and constraints Furthermore, the prototype also implemented two new features to encourage users to discover ideas in different spaces of possibilities. The first, called Insights, discovered information from different external sources in response to the simple keywords entered to filter the examples case studies associated with the selected business model type. The algorithms that retrieved this information codified creativity knowledge as expansions of queries with which

114 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller to discover content related more tangentially to the keywords. The information was presented on different interactive cards populated automatically with content extracted from different curated channels (e.g., academic papers from Google Scholar and videos from YouTube). The algorithms called public APIs to these providers to retrieve metacontent about the papers, podcasts, and videos – metadata such as titles, formats, and, where appropriate, duration. The prototype presented this content to users using information cards. The cards were designed to have a similar presentation layout to the cards describing the case studies. Examples of them are depicted in Figure 6.9. Clicking on the card title opened the selected paper, podcast, or video in a separate tab to the browser. Users were able to use simple interactive features to, for example, filter the content sources accessible via the cards. The Insights feature was implemented to guide users primarily to generate Mini-C and Little-c creative outcomes in the form of ideas that others might not consider novel but can contribute to future Pro-C solutions or to generate new meaningful reflections that can also contribute to future Pro-C solutions. The second feature, called Transformations, was introduced to guide users to undertake transformational creativity thinking about their business model and strategies. According to Boden, exploratory creativity assumes a

Figure 6.9 The prototype’s presentation of information cards that provide different insights into the entered business challenge

AI-enabled support for creative thinking about business models 115 defined space of partial and complete possibilities to explore – a space that also implies the existence of rules that define the space. Changes to these rules produce what might be thought of as a paradigm shift called transformational creativity (Boden, 1990). Therefore, to encourage this transformational creative thinking, the prototype also presented four different questions that directed users to change one or more rules that were defining their space of ideas being explored in order to open up new spaces of possibilities. These rules – related to people, places, organizations, and other concepts associated with their businesses – were designed using creativity knowledge about transformational creative thinking techniques. For example, one published constraint removal technique (Onarheim, 2012) directs users to discover then deliberately remove constraints that limit the space of possible ideas, then to generate ideas in this enlarged space of possibilities. The operational knowledge of how to implement this technique was codified in simple rules and interactive guidance for the user. Figure 6.10 shows four questions that had the potential to be generated by the prototype. Examples of these questions were “Imagine if you were not limited to the current leadership model, how might you address the challenge?” and “How would you approach this challenge if you were not restricted to the physical venue of your business?” Again, the Transformations feature was implemented to guide users primarily to generate Pro-C and Mini-C creative outcomes. Finally, during all interactions with the prototype, the user was able to access, add to, and download inspirations from the Inspiration List. This list was accessed by clicking on a lightbulb icon on the right side of the page. When clicked, the main content page narrowed and the list became visible to the right of it (see Figure 6.11). During interactions with the prototype, a user was able to add business model types, case studies, and themes to the list, at the

Figure 6.10 Guidance provided by the prototype’s Transformations feature

116 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller

Figure 6.11 The Inspiration List, which can be accessed on the right side to record discovered businesses, case studies, themes, and generated ideas

click of a button, then add notes to these additions as inspirations for further creative thinking. Summarizing, the prototype combined codified creativity knowledge in the form of creative searches and interactive guidance with information about business model types and related case studies. It was co-designed with business users. However, feedback on the prototype was still missing. Therefore, the authors conducted first formative evaluations of the prototype with business users who had not previously accessed the prototype to provide qualitative feedback on its concepts, aim, and features. These first evaluations are summarized in the next section.

First evaluations of the BOB prototype The reported version of the BOB prototype was evaluated by four business professionals from different organizations. The current roles, sectors, and location and the total years of work experience of each of these professionals are listed in Table 6.1. Three had more than twenty years of work experience in different business sectors and were located in the United Kingdom. Each professional engaged with the prototype individually, and each evaluation session took place online, remotely, using screen sharing through Zoom. Each session took between 40 and 60 minutes. During each session,

AI-enabled support for creative thinking about business models 117 Table 6.1 The current roles, sectors, and location and the total years of work experience of each of the business professionals at the time of the first prototype evaluation Role

Sector

Years work exp.

Location

Operations manager Company executive Founder/business strategist Managing director

Brand supplies Digital technology Social/creative Market research

4+ 25+ 20+ 25+

Dubai UK UK UK

the business professional was asked to perform tasks from a prepared test script and to “think out loud” as they performed these tasks. Each test script was adapted slightly to the individual. It was flexible and allowed the professionals to explore different features and undertake different actions. When this occurred, unscripted and/or adapted questions were asked in response to what was observed and/or said, and prompts were given to guide the professionals back to the scripted tasks. All of the sessions were recorded, and the combined screen and think aloud audio recording were reviewed to collect both positive and negative feedback about the prototype. All four of the business professionals were able to use the tool and undertake the scripted tasks. The tasks required each to bring a business challenge then to walk through the prototype’s features to explore that challenge. The audio recordings revealed that the professionals were positive about the prototype as a whole. Both the concept and the premise behind it – creative guidance to think differently about business models and strategies – were deemed valuable and of interest. Many professionals responded that they would return to use such a tool in their professional work. Furthermore, different features of the prototype were singled out as having specific potential value. The digital support to explore different business case studies was reported to be potentially very helpful. Both the detailed case study content and the associated interactive themes were also reported to have value. In addition, the professionals were positive about the different content types and formats, such as videos and podcasts, which were available to explore using the Insights feature. These different content types were reported to be both unexpected and surprising. Furthermore, the questions presented to encourage more transformational creativity using the Transformations feature were reported to be engaging, of interest, and useful. And the right-side Inspiration List was described as a great device to store, reflect on, and share ideas and sources with colleagues. That said, the professionals also reported that had each of them been able to enter more information about a business challenge, then the prototype might have provided them with more complete and more relevant guidance. When asked to elaborate, many reported that the simple business challenge was perceived to limit the information entered into the prototype to reason with. Similarly, many of the professionals reported that the discovered insights content

118 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller was often interesting but not sufficiently related to the entered challenge, and this introduced a lot unwanted noise into their tasks. Moreover, in spite of positive feedback about the prototype’s overall concept, premise, and features, the professionals reported other problems which formed barriers to adoption. Much of the information presented by the prototype (e.g., about the business model types and case studies) was perceived to be inaccessible and/or difficult to understand. The business model descriptions were reported to lack important information, such as about growth in terms of the stages that businesses often need to go through. Unsurprisingly, the business professionals considered themselves to be intelligent and knowledgeable about their businesses and challenges. However, their interactions with the prototype were reported to create a sense of not understanding these challenges sufficiently. Likewise, although the wizard-led process for selecting a business model type in response to simple choices was perceived to be intuitive, some of the questions asked by the wizard were unclear and the associated examples available to explain these questions did not help. Furthermore, the rationale for the prototype asking some of the questions, such as whether the product or service was physical or digital, were not understood, and some of the business professionals wanted to be able to answer these questions differently (e.g., to be able not to answer or to provide a middle-of-the-range value). Some also questioned the absence of other questions that they reported to be important in business model selection, such as about the size of the business requiring the model. Furthermore, much of the prototype’s generated guidance was perceived to be irrelevant to the entered challenge. Although the case studies were valued, comments were also made about their range (which was too limited or from the wrong sectors), their validity (which was not sufficiently up-to-date), their poor fit to the presented business model type, and the inappropriateness of the extracted entities (which were not related closely enough to the userentered challenge). Similarly, the Transformations questions to encourage transformational creative thinking were insufficiently refined and/or adapted to the entered challenge and/or the discovered case studies. However, the prototype feature that received the most comments was the Insights feature. In spite of the potential and excitement that the feature generated, some of the returned results were not relevant. In response, constructive feedback from the professionals included the inclusion of keyword searches (e.g., to discover related information about the entity presented in the bottomright of each card), knowledge level filters (e.g., expert versus entry-level) to provide more user control over the discovered insights, and tagged content from paywalls (to avoid friction during tasks). The visual presentation of the information cards was also reported to be too uniform and dry. And finally, to encourage more collaborative thinking about business models and strategies, the prototype should enable other users in the same organization to explore other user results, to generate what was called “tribal knowledge”, and the saying “If only we knew what we know”.

AI-enabled support for creative thinking about business models 119 To conclude, this first evaluation with the four experienced business professionals revealed the potential value of even limited interactive features to encourage creative thinking about businesses and their models. The overall design was usable and revealed that the core design ideas were valid. However, perhaps unsurprisingly given the first version prototype, the execution of the design was relatively poor, and substantial reworking of parts of the interaction, algorithms, and curated content were needed. Indeed, these evaluation results demonstrate how sensitive business users might be to co-creative AI products in tasks such as business model selection. Therefore, to understand better how digital tools such as BOB might be used in businesses, semi-structured interviews were conducted with a different ten business professionals from different organizations. Each professional was asked the same questions, then offered a chance to engage with the same described and evaluated version of the BOB prototype individually. The current roles, sectors, and location of each of the professionals are listed in Table 6.2. All the interviews were conducted individually online and allowed for indepth exploration of topics. During each interview, each business professional was asked about their business and role in that business, about the business’s approach to developing its business model, and whether creative thinking and/ or digital tools played a role in developing the business model. At the end of each interview, each business professional was walked through the BOB prototype and asked questions about its features and guidance. More details are reported in Chandras (2022). In this chapter, we report only the results from an analysis of the audio-recorded transcripts that identified three usage scenarios in which future versions of the BOB prototype can add value to businesses. Each scenario is described in turn. Table 6.2 The current roles, company profiles, and sectors and location of each of the business professionals at the time of the second prototype evaluation Current role

Company profile and sector

Location

Partner Consultant

Large company, working across sectors Start-up consultancy supporting other start-ups SME developing not-for-profit digital technologies Start-up developing a digital health app Start-up supporting creative production and consultancy SME developing not-for-profit digital technologies SME working in the education technologies sector SME agency working in experiential marketing SME working in the education sector

Global UK

Innovation manager Business development Business founder Business development Business founder Business founder Business development

UK UK UK UK UK UK UK

120 Mark Dowsett, Neil Maiden, and Charles Baden-Fuller The first scenario was to support strategy and lead generation by startup founders to discover more creative business opportunities. These founders reported often encountering challenges during early business development to craft or evolve their business model and strategies. A need for guidance to select a business model, combined with personalized diagnostic features to develop the business’s needs were identified. These features should include multimedia support for explaining key business concepts and different business templates customized to each the start-up. Support for cash flows, such as guidance for finding monetization mechanisms like freemium or joint business partnerships, was also needed in this scenario. The second scenario was quite different and supported client-facing consultants and business development team members to upskill to deliver pitches to clients. People in these roles are often required to come up with winning pitches that solve client business challenges, so they need to update the knowledge and skills with sector- or subject-specific information, data, and insights. The third scenario described how consultants and other stakeholders might work together to share perspectives and crowdsource ideas with which to solve business problems. Two reported challenges with large client teams were poor access to stakeholders and lack of stakeholder’s creative engagement to contribute fresh perspectives and ideas. Therefore, future versions of the prototype will need to motivate then guide stakeholders to participate effectively in collaborative processes, share perspectives, and generate new ideas as part of a collaborative creative process.

Conclusions and next steps This chapter has reported early design research that integrated codified knowledge about established creative thinking processes, techniques, and tools with digital information about business models from the existing Business Model Zoo. The result was a new digital prototype called BOB that was designed to support business users to generate new ideas about their business models. BOB was an example of co-creative AI, a form of human-centered AI in which machine and human reasoning interleave to solve complex problems. It was designed to support business professionals to generate different forms of ProC, Mini-C and Little-c creative outcomes (Kaufman & Beghetto, 2009) related to their business models, systematically and regularly. However, in first evaluations, although the professionals were positive about the concept behind BOB and its features for creative thinking support, the early prototype needed to be refined to demonstrate its potential to generate the different forms of creative outcomes. Similar barriers to use were encountered when deploying co-creative AI tools in other professional domains (e.g., Maiden et al., 2020b). The next stages of this design research will be to develop new versions of the BOB prototypes to support business professionals working in the three

AI-enabled support for creative thinking about business models 121 scenarios reported in the second study. Indeed, this development work has already started. Each is being designed to integrate into the reported workflows of business leaders and consultants and to provide more creative guidance about business strategies and tactics as well as models to these users more quickly than in the current prototype. We look forward to reporting these prototypes and their evaluations in the future.

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7

Conclusions and future directions Margherita Pagani and Renaud Champion

As we come to the close of this book, Artificial Intelligence for Business Creativity, it is clear that the integration of AI into business operations holds immense potential for driving transformative change and innovation. The journey we have taken through the various chapters has illuminated the many ways in which AI can foster creativity and inspire new ideas, from challenging the status quo and finding new ways to engage with customers and stakeholders, to optimizing processes and fostering collaborations. But as we look to the future, it is also clear that there are many challenges that managers have to face, such as lack of understanding or knowledge about AI, data challenges, integration challenges, ethical and legal considerations, resistance to change, lack of skills and expertise, and difficulties in measuring and monitoring the performance and impact of AI systems. These challenges must be addressed head-on for organizations to effectively implement AI and drive their businesses’ metamorphosis into more successful and adaptable entities, ready to take on future challenges. In this final chapter, we will reflect on the key takeaways of the book and explore the opportunities and challenges that lie ahead as we continue to harness the power of AI to drive business creativity. Throughout the various chapters of this book, we have delved into how AI can foster creativity and inspire innovation. From examining the potential for AI to make us humans more creative, to exploring how AI systems can inspire new venture ideas generation as well as investigating the role of AI in stimulating continuous and ongoing innovation, optimizing processes and procedures across teams, and facilitating and increasing organizational collaborations and alliances. Additionally, we have presented a proposed typology and new directions for academia-industry collaborations in the field of AI and creativity in marketing as well as examined the potential for AI-enabled support for creative thinking about business models. Overall, the research presented in this book suggests that AI has the potential to act as a powerful engine for not only inspiring creativity but also enabling and accelerating it. It can help organizations stay ahead of the competition and meet customers’ evolving needs by creating new products, DOI: 10.4324/9781003287582-10

126 Margherita Pagani and Renaud Champion services, and experiences that can improve people’s lives and solve important social and economic problems. In summarizing the main managerial lessons that emerge from this book on how AI can help managers change dimensions to cope with today’s major issues in a creative way, we have identified the following critical ones: • AI and deep tech as instrumental resources for making sense of large amounts of data: AI algorithms, such as machine learning and natural language processing, can analyze vast quantities of data and extract insights and patterns that may not be immediately visible to humans. With the promises of quantum computing, they can help managers to calculate and analyze data more quickly and efficiently, particularly for tasks such as optimization and modeling problems. Additionally, they can help managers to automate certain tasks, freeing up time and resources for more creative and strategic initiatives. • AI and deep tech as inspirational resources for more innovative solutions: generative Generative AI models can be used to generate novel concepts and ideas based on patterns and trends in data. This can be particularly useful for tasks requiring the generation and evaluation of many different ideas, such as optimization and design problems, and can help humans think more creatively by providing new perspectives and insights that may not have been previously considered. By leveraging AI’s ability to generate and evaluate novel concepts, managers can come up with more innovative solutions. • AI and deep tech as state-of-the-art resources to open new fields of experimentation: virtual and augmented reality alongside Web3 technologies help managers design new kinds of customer experience or product simulation. Robotics and cyber-physical systems can solve many business problems in areas as diverse as inspection and maintenance, healthcare, agriculture, manufacturing, or supply chain management, in a more efficient and safer way. We look forward to further research in this area and the continued development of AI’s capabilities in supporting business creativity. As the field of AI continues to evolve and advance, it will be important for managers and business leaders to stay informed and stay ahead of the curve to fully leverage the capabilities of AI in their organizations. However, there are also several managerial challenges that organizations may face when leveraging AI for business creativity. These include: • Ensuring that AI is used ethically and responsibly taking a proactive approach to identifying and addressing potential negative impacts, fostering transparency and communication, and adhering to ethical principles in the design, development, and deployment of AI.

Conclusions and future directions 127 • Ensuring that the development and implementation of AI has a positive impact on society considering the potential consequences of its use and proactively addressing any negative impacts that may arise. This means fostering transparency and communication to educate the public and decision-makers about the capabilities and limitations of AI. By doing so, we can work toward a future where AI is a force for good for the full society, improving lives and driving progress for all. • Addressing concerns about the impact on the workforce. The integration of AI into business operations may lead to the automation of certain tasks, which can raise concerns about job displacement and the way work is performed and the skills that are required. This can require managers to retrain and reskill their employees and adapt to new ways of working. • Managing the integration of AI into existing systems and processes. AI can be a powerful tool, but it also requires careful planning and integration to ensure that it is used efficiently aligning it with the business goals and objectives. • Managing data security and privacy. As AI relies on large amounts of data, it’s important to ensure that this data is protected and that privacy is respected. • Anticipating the impact of AI on legal-related issues for the company, including intellectual property rights in the case of AI-generated products or liability management in the case of defects and damages induced by such products or services. By carefully addressing these challenges, organizations can effectively leverage the power of Artificial Intelligence and emerging new technologies to foster business creativity in a responsible and ethical way with due regard for its potential impact on society and the workforce. AI can be a powerful tool, but it’s important to remember that it is not a magic solution to all of our problems. And most importantly, it can assist humans but not replace humans entirely. Tasks such as project management and team communication are unlikely to be automated anytime in the near future. Overall, the key to leveraging the power of AI for business creativity is to understand how it can augment and enhance our human capabilities rather than replace them. By harnessing the power of both human and machine intelligence and respecting ethical principles, we can create new and innovative solutions that drive progress and create value for all stakeholders. As we move into the future, it’s important to embrace the opportunities that AI presents and to stay open to new ways of thinking and working. By doing so, we can unleash the full potential of artificial intelligence and harness its power to drive creativity and innovation in the business world. As you reflect on the ideas presented in this book, consider how you can harness the power of AI in your own business and organization. What steps can you take to effectively integrate AI into your operations and strategies in

128 Margherita Pagani and Renaud Champion a responsible and ethical manner? How can you leverage the capabilities of AI to drive creativity and innovation in your field? Think about what you can do to stay ahead of the curve and embrace the opportunities that AI and deep tech present for the future. As we close the pages of this book on AI for business creativity, let us remember that the true power of AI lies not in its ability to automate and streamline but in its potential to inspire and innovate humans. The future of business belongs not to those who simply adapt to the rise of AI but to those who dare to harness its full creative potential. So let us embrace the disruption, let us question the status quo, and let us use AI to push the boundaries of what is possible. Creativity is a human skill that may drive the evolution of technology to enable progress and innovation in society.

Appendix 1 Legal issue of AI for business creativity Olivier Lasmoles

Creativity, which is “the ability to produce new and useful ideas” (Pagani & Champion, 2021, p. 1), is instinctively a very human thing. Advances in AI systems have shown that this is not the case, and that these systems, in addition to being intelligent assistants, can be “sources of creativity” (Pagani & Champion, 2021, p. 1). Artist Henri Matisse explained that “to create is to express what is within oneself. All authentic creative effort is interior. . . . The work of art is thus the result of a long process of elaboration” (Matisse, 1972, p. 321–322). Now, whether a work of art or not, creation means protection. It is, therefore, a question of copyright and the conditions for protecting the work created. The originality of the work to be protected is that it is the direct result of a tool that works through a code. However, literary and artistic property was codified in 1957, at which time questions of creation by a machine were not yet relevant. Can the conditions set out in the Intellectual Property Code be transposed to this new situation or will we see an evolution in the notion of copyright? This question is of interest not only to lawyers but also to companies and their managers. Because beyond the protection regime of a work, the question arises as to whether a creation resulting from an AI can be considered as a protectable work. This assumes that the three conditions of the Intellectual Property Code are met. Creation must, first of all, be a work of the mind. Does using a computer to create a work change the nature of that work? Does the completion of Beethoven’s 10th symphony, thanks to an AI which, with the help of musicians, was able to reconstitute two 20-minute movements and arrange the score for orchestra, make it a different work in nature from the 7th symphony by the same Beethoven? Indeed, “the idea of the work is primordial” (Walvarens, 1999, p. 18); it cannot come from a computer that can only use the rules. The concept belongs to a person; its realization can belong to the computer. This is particularly true of conceptual art, which places the idea, the choice, at the heart of every artistic project. To be protected, a work must also be characterised by its originality. The difficulty for the creation of an AI is to determine the personality of its author

130 Olivier Lasmoles as jurisprudence has long demanded. Well before the appearance of AI, the Court of Cassation relaxed the notion of originality by considering that a work was original if it was possible to detect the author’s personalized effort and intellectual contribution (Cass. Ass. Plén., 1986). The judges went even further by considering that external elements could be retained to qualify a work as original. Thus, the High Court of Nanterre acknowledged that recognition in specialized circles (exhibitions, awards, press, etc.) led to recognition of the original character of the work (TGI, Nanterre, 2019). In European Union law, a work is original if it is the author’s own intellectual creation. There is thus a unification of rights. If we are, therefore, witnessing an objectification of the criterion of originality, a work created by an AI will be so because preparatory work will have been carried out by a physical person or a group. This work makes it possible to reintegrate the human element into the criterion of originality. However, in this type of work, are the choice of the algorithm, the establishment of the computer program, and the selection of the data – all preparatory acts within the meaning of the case law? Can they be seen as acts that fall within the criteria for selecting between works that will be protected and others? The main difficulty is that no founding text of copyright in France gives any selection criteria. French law is still searching for such criteria; no doubt one or more flexible criteria should be adopted. The last condition for the protection of a work is the presence of an author. Without an author, there is no originality and, therefore, no work. Unlike in Common Law countries, in French law, the author is the person who actually created the work. But this notion could change. For this, could we not refer to the vision that prevailed until the 16th century? The work was attributed to a person responsible for a workshop, a group of craftsmen or artists. The completion of Beethoven’s Symphony No. 10 was the result of a collaborative effort. Thus, could the notions of author and attribution criteria not evolve to include these questions of collaboration and AI? A final line of thought, quickly ruled out, would be to endow AI, like robots, with a legal personality, allowing them to benefit from the title of author. AI is forcing a change in the regime of intellectual protection, of acts of creativity. We have discussed the issue of works of art, but what about the fears of creative acts asked of students during their studies, faced with tools such as ChatGPT? If these questions seem new, they are not. Manuel Barbadillo said in 1982: “I have already said in the past that all artists will end up using computers. . . . artists themselves are unprejudiced. They are used to questioning the scale of values. It is even the essence of their work” (Barbadillo, 1982, p. 5).

References Barbadillo, M. (1982). Paintings and drawings. Castillo del Bil-Bil Cultural Centre, Malaga, p. 5.

Legal issue of AI for business creativity 131 Cass. Ass. Plén. 7 March 1986, SA Babolat Maillot Witt c/ Pachot. Matisse, H. (1972). Ecrits et propos sur l’art, Hermann Paris, coll. Savoir, pp. 321–322. Pagani, M., & Champion, R. (2021). Comment l’IA peut booster la créativité de votre entreprise. Harvard Business Review France. https://www.hbrfrance.fr/chroniquesexperts/2021/05/35617-comment-lia-peut-booster-la-creativite-de-votre-entreprise/ TGI, Nanterre, 19 December 2019. Walvarens, N. (1999). La notion d’originalité et les œuvres d’art contemporaines, Revue Internationale du Droit d’auteur, p. 18.

Index

Note: Page numbers in italics indicate a figure and page numbers in bold indicate a table on the corresponding page. 3D-GAN 48 5G 1 academia-industry collaborations 4, 82–93, 90–91, 125 accelerated discovery 70, 76 Accenture 17; “My-Wizard” 17 Adobe 19 advertising 4, 13–15, 35, 88, 92–93, 99; see also return on advertising spend (ROAS) 2 aerospace domain 70, 78 Affectiva 14 AI: in agriculture 21; to analyze data 14, 18–20; to analyze facial expressions and body language 15; to automate 17, 19–20; and business creativity 66–67, 79, 85, 89, 126–128; for challenging status quo 12–13; for consumer understanding 13–14, 66; and creative new venture idea generation 56–59; and creativity 4, 9, 83; and creativity in marketing 4, 82–93, 125; for customer engagement 21; and deep tech 126, 128; definition 82–83, 102; for design of ideal organizational architecture 17; to design 38–39; to disrupt industries 12–13, 125; in drug discovery and development 19;

and employee creativity 85–88, 91; in energy 21; in enhancing innovation 11–20; and experimentation for breakthrough innovation 19–20; fostering business creativity 4, 65–79, 83, 87, 90–91, 93, 125–128; in healthcare 21; human-AI interaction 37, 102; human-centered (HCAI) 101–102, 120; and human creativity 3, 24–41; impact on business creativity 69; to improve creative thinking 3; for innovative product and service solutions 15–16, 93; inspiring individual creativity 37–40; legal issues 129–130; and machine learning 2, 12, 14–15, 18, 24; in marketing 88, 90; to mobilize/orchestrate innovators 17–18; in new product design 70–73, 73; for orchestrating innovation efforts 18–19; in popular culture 9; in process design 74; strong 83; tools 10, 13, 34, 72, 78–79, 102–103, 120; in transportation 21; to train 40; for value-creation initiatives 16–17; weak 83; see also AI art; AI-augmented; AI systems; AI technology; generative AI AI art 28, 34, 71; painting 32, 36

Index 133 AI-augmented: diagnosis systems 33; management software 31; work environments 3 AI-DA robots 31, 71 AI systems 1, 20–21, 24–28, 25, 38–41, 47–62, 55, 65, 69–77, 102, 125; average creativity 59; combining concepts 31, 32, 33, 37, 40; and creative productivity 58–59; creativity in 28–33; data collection by 24–25; definition 48; ideating to build novelty 31, 33, 37, 40; impact on creative practice 33–36; as inspirator and teacher 35; mimicking human cognition 29–31, 31, 36–37, 38, 40; as new instrumental resource 34; rule-based 74; as tool to deconstruct creative process 35–36; as tool to explore possibilities 34; see also generative AI systems AI technology 13, 83, 88 Aiva (Artificial Intelligence Virtual Artist) 33 algorithms 3, 12–13, 15, 17, 19, 21, 28–29, 31, 36, 37, 38–39, 47, 51, 66–67, 71–72, 75–76, 82, 88, 101, 104–105, 108, 113–114, 119, 126, 130; AI 19, 21, 75–76, 88, 101, 126; deep learning 28, 33, 103; entity extraction 113; generative design 103; machine 66; machine learning 12, 14–16, 21, 37, 71, 102; quantum 89; unsupervised learning 71, 73 Amazon 10, 16, 100, 106; Go store 15 Amelia AI 16 Apple 10, 66, 99 artificial creativity see computational creativity Artificial Intelligence see AI augmented reality (AR) 77, 126 Autodesk 38–39, 71, 75 automation 2, 24, 31, 32, 39, 74–75, 87–88, 90, 92, 101, 105, 127; intelligent factory 31, 39; process 75

autonomous ground vehicles (AGV) 31, 32 autonomous systems 1, 31 autoregressive language models 28 average creativity 57–59 blockchain 1 brain networks 26; Executive Attention Network 26; Imagination Network 26; Salience Attention Network 26 breakthrough innovation: AI and experimentation of 19–20 business creativity 1, 3–5; and AI 4, 65–79, 83, 85, 87–88, 89, 90, 90–91, 92–93, 125–128; how AI fosters 65–79; definition 66–67; factors 67–68; impact of AI on 69; key skills required for 89; legal issue of AI for 129–130; in Walmart 66–67 business model finder 108–110, 109 business models 4, 11, 48, 54, 55, 93, 99–101, 104–113, 109, 115–120, 125; see also business model finder; Business Model Zoo (BMZ) Business Model Zoo (BMZ) 101, 106–110, 107, 108; see also Business Opportunity Builder (BOB) Business Opportunity Builder (BOB) 101, 107–108, 110, 111, 112, 113, 116–120, 117, 119; Insights 113–114, 117–118; Inspiration List 115–116, 116, 117; Transformations 114–115, 115, 117–118; see also business model finder business sector 116 business strategies 67, 79, 99–101, 104–107, 114, 117–118, 117, 120–121, 127 Carer app 103 chatbots 3, 15, 25, 74, 76, 87–88, 90, 92 ChatGPT 9, 15, 20, 28, 48, 55, 71, 130 Cisco 17–18 Codex 72 cognition 49, 50; emotional 68; human 29–31, 31, 36–37, 38, 40; social 26

134 Index cognitive engagement 76–77 cognitive flexibility 4, 48, 54, 56–61; AI-enhanced 56–59 cognitive insights 75–76 cognitive interactions 77 cognitive persistence 54, 56 Common Crawl 56 Common Law countries 130 computational creativity 4, 25, 25, 27–28, 33 concurrent engineering (CE) framework 4, 65, 69 consumer needs 13–14 consumer research 13–14, 82 content creation 87 copyright 129–130 creative AI 37; business impact of 2–3; co- 102–104, 120 creative industries 4, 25, 82 creative problem-solving 99, 104 creative processes 25, 27, 104–105 creative techniques 104–105 creative thinking 1, 3, 25–26, 48, 54, 99–105; about business models 4, 99–121, 125; exploratory 113; transformational 115, 118 creativity 49, 99–101; accidental 33; age and 87; in age of AI 3–4, 9–21; AI-assisted 92; in AI systems 28–33; assumption busting 104; asymmetry 105; Big-C 99; cognitive theories of 53–54; collaborative 103; collective 86; combinational 104–105; computing 1; constraint removal 103–104, 115; definition 24, 26, 66, 82–83, 99–100; design 28; and emotional intelligence 82; emotions and 87; and empathy 25; enhancing 4, 20–21, 68; exploratory 104–105, 113–114; factors influencing 67–68; heuristic ideation 105; in human brain 26–27; individual factors 67–68; as intrapersonal intelligence 2; intelligence and 86; knowledge 100–102, 104–105, 107, 113, 115–116; Little-c 100,

103, 107, 111, 113–114, 120; marketing 83, 90–91; in marketing 82–93, 84; Mini-C 100, 107, 113–115, 120; observed 4, 25, 25, 40; organizational 65, 69, 79; organizational factors 67–68; pathways to 54, 56; playfulness 105; Pro-C 100, 103, 107, 111, 113–115; psychology and 85–86; skills 88, 90, 92; storyboards 105; tools 2, 11; transformational 104–105, 114–115, 117–118; triggers technique 105; TRIZ inventive principles 103, 105; verbal fluency and 86; workforce 3; see also average creativity; business creativity; computational creativity; digital creativity; employee creativity; entrepreneurial creativity; human creativity; individual creativity customer churn analysis 3 customer data 13–14, 19 customer engagement 16–17, 21, 100 customer needs 10 customer satisfaction 14, 17 cyber-physical systems 126 cybersecurity 74 DALL•E 2 31, 32, 48, 55, 56, 71 data processing abilities 72–73, 73 deep learning 1, 28, 31, 31, 33, 48, 50, 93, 102; see also deep product learning deep product learning 70–71, 73 deep tech 126 diffusion 31 digital age 99, 105 digital assistant conversational content 88, 92 digital creativity: and co-creative AI tools 102–104 digital economy 24 digital platforms 1, 47 digital twin 78–79 digital voice assistants 3 drones 3, 21

Index 135 Dynamic HomeFinder 103 ecosystems 77–78; new 69, 77 Einstein AI 14, 17 emotional intelligence 82, 87, 89, 92 employee creativity 67, 82–83, 90, 90–91, 92; and AI 85–88; at individual level 85–87; managerial implications 92–93; at organizational, societal, and market levels 87–88; skills required for 88–89 entrepreneurial creativity 4, 48–50, 49, 61–62; generative AI systems and 50–53, 54–56, 55, 56, 60, 61–62; creativity process 48–50, 49 entrepreneurs 34, 47–49, 55, 56–57, 59, 61 entrepreneurship 34, 58, 61 ethical issues 20 ethical principles 126–127 European Commission 90 European Union 20; law 130 experimentation 11, 19–20, 126 extended reality (XR) 77 Face Synthesis technology 30 facial recognition technology 14, 31 first-mover advantage 49 flexibility pathway 53–54, 56–58 FootwearGAN 52–53, 55 generating new solutions 71–72, 73 Generative Adversarial Networks (GAN) 28, 48, 53, 70, 73 generative AI 20, 49, 58–61, 71–72, 89, 126; see also generative AI systems generative AI systems 4, 31, 48, 50, 58–59, 71–72; and entrepreneurial creativity 50–53, 54–56, 55, 56, 60, 61–62 Generative Diffusion Models 48 Generative Pre-trained Transformer 28, 51; models 48; see also ChatGPT; GPT-3 GitHub 17; “Copilot” 17

globalization 82 Google 10, 18, 99, 106, 113; AlphaGo 15; Arts & Culture Lab 29, 29; AutoML 18; DeepMind 15; Scholar 114 governance 20, 78–79, 101 GPT-3 15, 28, 32, 48, 50–51, 56–57 group mood 50 Huawei 33 human creativity 3, 24–25, 25, 27–28, 31, 33–34, 37, 39, 76, 83, 88–89, 90, 91–92, 101 human values 3 IBM 5, 17–19, 65, 70, 74, 78; Collaborative Product Development 77; Quantum System One Flexi cables 73; Watson Assistant for HR 17; Watson Studio 18–19 IdeasAI 50–51 ideation 39; heuristic 105; tools 21 individual creativity 3–4, 25, 37–41, 86–87, 90; agile methods 38, 41; imagination 39–40; out-ofthe-box thinking 39 Innocentive 17 innovation 38–40, 47, 65–67, 69–70, 72–73, 82, 86–87, 91–92, 99, 125, 127–128; in age of AI 3, 9–21; AI-based 93; AI-driven 4; AI’s role in enhancing 11–20; business 92, 99; creative 86; guidelines for enhancing 10–11; manager 119; strategic 87; see also breakthrough innovation; innovation efforts innovation efforts 11; AI for orchestrating 18–19 innovators 11–12, 20; AI to mobilize/ orchestrate 17–18 Instagram 51 Intellectual Property Code 129 intelligent autonomous systems 31 inventory: levels 14; management 16, 66 IS domain 91 JECT.AI 103

136 Index Kartell 38–39, 71 LaMDA 48 latent space 52 leadership style 68 legitimacy 49 Lexus 83 Living Archive 29, 29, 30 logo design 4 LyricJam 31–32 machine learning (ML) 2, 12–16, 18–19, 24, 27, 29, 31, 34–35, 36–37, 47, 66, 71, 74, 75–76, 93, 102, 126; quantum 47 Managers of the Future (MoF) 3 marketing 19–20; AI in 88, 90; automation 87; content 87–88, 90, 92; creativity 4, 82–93, 90–91; creativity in 82–93, 84, 125; experiential 119; international 82; materials 14; processes 2, 76; research 13–14; semantic 88; social media 4, 87, 90; strategies 87; teams 93; traditional 13, 92; viral 4 metaverse 78–79 Midjourney 31, 55, 56 MindMeld 17 mixed reality (MR) 77 MuseNet 32 natural language processing 16–17, 20, 33, 74, 76, 83, 126 Netflix 12–13, 15, 39 neural networks 70, 102 New Balance 52–53 new collaborations 69, 77 new process design 69, 74–77; role of AI in 74 new product design 4, 52–53, 55, 55, 69; AI in 70–73, 73 new venture ideas 4, 47–62, 55, 56, 60, 125 Noldus Information Technology 15 NotCo 52 observed creativity 4, 25, 25, 40 OpenAI 15, 20, 51, 71

open-source code 1 optimized processes 69 organizational architecture 11; AI for 17 organizational climate 68 organizational collaboration 69, 76, 125; new 77–79; in metaverse 78–79 organizational creativity 65, 69, 79 organizational culture 68, 88, 90, 92 organizational silos 65 originality 27, 51, 85–86, 88, 129–130 pandemic 74, 76, 99 Pantheon Lab 30, 30 pattern recognition 102 Pencil 83 persistence pathway 53–54 pharmaceutical industry 47, 70, 78 predictive analytics 76 predictive models 12 price optimization: real-time 3 PRISMA study 84 privacy 127 product and service solutions: AI for innovative 15–16 product design 4, 52, 55–56, 55, 69, 69, 67, 72, 73, 74, 77, 92 product development 17, 47, 71, 77 product distribution 17 product promotion 17 profitability 82 prompt 28, 41, 56, 71–72, 117 quantum computing 1, 70, 72, 73, 77, 89–90, 90, 126 Quid 18 Realeyes 15 real world 78–79 Reeps One 31 regulatory considerations 11, 20 resources 19, 21, 24, 28, 49, 68–69, 73, 76, 87, 93, 100, 105, 111, 126; AI-specific 69; external 17; human 69; inspirational 126; instrumental 126; intangible 69; internal 17; required 17; state-ofthe-art 126; technical 69

Index 137 return on advertising spend (ROAS) 2 revenue models 11, 16–17 Risk Hunting app 103 risk management 12, 76 robotics 1, 40, 126 robots 3, 31, 32–33, 33, 35–36, 37, 71, 75, 77, 130; surgical 33 rule-based AI systems 74 Salesforce 14, 17; Einstein 14, 17 Scopus 84, 84 security domain 74, 78 semantic marketing 88 sentiment analysis 3 service design 4, 69, 92 Siemens 19 Silicon Valley 51 social media marketing 4, 87, 90 social media mining 3 speech and voice analysis 14 Spotify 12–13, 15, 39, 100, 106 Stable Diffusion xiv Starck-Kartell-Autodesk 38–39, 71 status quo 10, 12–13, 125, 128 STEM 79 stimuli providers 50, 53, 55–56 storytelling 9 strategies 105–106; business 67, 79, 99–101, 104–107, 114, 117–118, 117, 120–121, 127; competitive 11; computeraided solution 72; creative 103; integrative 92–93; marketing 87; product development 71;

replenishment 66; retention 76; risk management 12 sustainability 82, 93 Tesco 14 Tesla 16–17 Transformer Model see Generative pretrained transformer models Trigger Shift 103 TRIZ principles 103, 105 Uber 10, 12–13, 66 value 5, 11, 27, 38, 57, 69, 79, 85–87, 91, 99–100, 102, 105–106, 107, 117–119, 127, 130; see also value chain; value-creation initiatives value chain 69, 106 value-creation initiatives: AI for 16–17 values 17, 68, 108, 130; see also human values video synthesis 30 viral marketing 4 virtual reality (VR) 77 Walmart 66; business creativity in 66–67 Web3 technologies 126 Web of Science (WoS) 84, 84 WhatsApp 100 workforce creativity 3 World Economic Forum 99 YouTube 114