Artificial Intelligence for Business: An Implementation Guide Containing Practical and Industry-Specific Case Studies 1032415088, 9781032415086

Table of contents :
Half Title
Title Page
Copyright Page
Contents
Preface
Contributors
Chapter 1. Introduction to AI in Business Applications
1.1 Introduction
1.1.1 AI Learning Types
1.1.2 Machine Learning
1.1.3 Deep Learning
1.2 Ethical and Security Issues in AI
1.3 AI Applications in Business Operations
1.4 Examples of AI Applications in Business
1.4.1 Smart Assistants
1.4.2 Helpdesk Chatbots
1.4.3 Face Recognition Technology
1.4.4 Personalized Recommendations
1.4.5 Predictive Maintenance
1.4.6 AI for Targeted Marketing
1.4.7 Smarter Supply Chains
1.4.8 Smarter Operations
1.4.9 AI-Enabled Quality Control and Quality Assurance
1.4.10 AI for Contextual Understanding
1.4.11 AI for Optimization
1.4.12 Sales and Business Forecasting
1.4.13 Security Surveillance
1.4.14 Spam Filters
1.4.15 Smart Email Categorization
1.4.16 Voice-to-Text Features
1.4.17 Process Automation
1.4.18 Social Media Insights
1.4.19 Vulnerability Exploit Prediction
1.4.20 Proposal Review
1.4.21 Billing and Invoicing
1.4.22 Customer Analysis
1.4.23 Market Prediction
1.4.24 Recruitment
1.4.25 Cybersecurity
1.5 Companies Using AI
1.6 Conclusion
References
Chapter 2. Digital Revolution and Sustainability without Well-Founded Mathematical Education?
2.1 Introduction - The Appearance of Mathematics in Corporate Management
2.2 Proposed Model - Decisions, Sustainability, and Mathematics
2.3 Methodology - Research Data Collection
2.4 Results of Analysis - Presentation of Results
2.5 Final Thoughts and Outlook
References
Chapter 3. Human Apprehension and Artificial Intelligence: Dilemma of Artificial Intelligence Fostering Human-Like Cognizance, Ethics, and Cognitive Capabilities and the Current Infusion of Artificial Intelligence in Business
3.1 Introduction
3.2 Humans and AI: A Scamper of Evolution
3.3 Infusion of AI in Businesses
3.4 Hawkeye vs. Ultron: A Concoction of Fiction and Actuality!
3.5 Consciousness and AI: A Humanoid's Tale
3.6 Ethical Paradigm and the Paradox It Follows
3.7 Conclusion: Assured or Incalculable Aftermath
References
Chapter 4. Artificial Intelligence in Marketing Applications
4.1 Introduction About AI
4.1.1 Consumer Perception on AI
4.1.2 Defining AI
4.1.3 Artificial Intelligence
4.1.4 Artificial General Intelligence
4.1.5 Narrow AI
4.1.6 Machine Learning
4.1.7 Deep Learning
4.1.8 Natural Language Processing
4.1.9 Natural Language Understanding
4.1.10 Signal Processing
4.1.11 Computer Vision
4.2 AI in Marketing Today
4.2.1 Usage of AI in Marketing
4.2.2 Programmatic Advertising
4.2.3 Transparency, Distrust, and Fraud
4.2.4 Omni Channel
4.2.5 Retargeting
4.2.6 The Influence of Social Media Marketing
4.2.7 Segmentation and Targeting
4.2.8 Facial Recognition
4.2.9 Interactive Marketing Through Biometrics
4.2.10 The Evolution of Marketing Analytics Toward AI
4.2.11 AI and Marketing Strategy
4.2.12 AI and Policy Issues
4.3 Conclusion
References
Chapter 5. Artificial Intelligence in Tourism and Advertising
5.1 Introduction
5.1.1 How Does AI Work?
5.1.2 Why Is AI Important?
5.1.3 What Are the Four Types of AI?
5.2 Ethics in AI
5.2.1 Ethical Challenges in AI
5.3 Tourism and AI
5.3.1 AI-Related Challenges in Travel and Tourism
5.3.1.1 Issues Associated with the Adoption and Use of AI by Tourists
5.3.2 Ethical Challenges of AI in Tourism
5.4 Advertising and AI
5.5 AI-Related Challenges in Advertising
5.5.1 Data Accuracy
5.5.2 Data Privacy
5.5.3 Changes in Customer Behaviour
5.5.4 Poor IT Infrastructure
5.5.5 Lack of Trust
5.6 Ethical Challenges of AI in Advertising
5.6.1 Privacy
5.6.2 Filter Bubbles
5.7 Conclusion
References
Chapter 6. Artificial Intelligence in Retail Marketing
6.1 Introduction
6.2 Literature Review
6.3 Tracing the Evolution of Marketing in the Metaverse
6.4 Immersiveness of Metaverse in Retail Marketing
6.5 The Sociability of Retail Marketing
6.6 Environmental Fidelity of Retail Marketing
6.7 Customer Expectations From Today's Marketers
6.8 AI-Human Intelligence (AI-HI) Relative Strength
6.8.1 AI's Strengths in Retail Marketing
6.8.2 HI's Strengths in Retail Marketing
6.8.3 AI-HI Collaboration in Retail Marketing
6.8.4 A Framework for Collaborative AI in Retail Marketing
6.8.5 Implementation of Marketing Technology
6.8.6 Technological Building Blocks of Retail Marketing
6.8.7 AI in Retail Market Applications
6.9 AI in the Retail Supply Chain
6.10 Statistics in the AI Space in the Retail Industry
6.11 Online Versus In-Store in Retail Marketing
6.12 Ethics in Retail Marketing
6.13 Future Scope of Retail Marketing
6.14 Conclusion
References
Chapter 7. Innovative Recruitment Strategies Using Knowledge Management Systems for Business Sustainability
7.1 Introduction
7.2 Literature Review
7.3 Understanding New Age Businesses
7.4 The Concept of KMS
7.5 Benefits of KMS in Recruitment
7.6 How KMS Can be Used in Recruitment
7.7 Innovative Approaches to Recruitment
7.7.1 Asynchronous Interviewing
7.7.2 Virtual and Augmented Reality
7.7.3 Robotics
7.7.4 Gamification
7.7.5 Chatbots
7.8 Future Trends in Recruitment
7.9 Best Practices for Using KMS in Recruitment
7.10 Organizations That Have Used KMS in Recruitment
7.11 Limitations
7.12 Conclusion
7.13 Questions for Class Discussion
References
Chapter 8. Human Resources
8.1 Introduction
8.2 Employee Attrition
8.3 Performance Management
8.4 Payroll, Benefits and Incentive Management
8.5 Intelligent Recruitment and HR Systems
8.5.1 AI in Recruitment
8.5.2 Bias in AI Algorithms
8.5.3 AI in HR Systems
8.6 WFM and Scheduling
8.6.1 Case Study: Using AI in HR - IBM
References
Chapter 9. Evolution of Chatbot in Human Resource Management
9.1 Introduction
9.1.1 Background
9.2 Literature Reviews
9.2.1 Research Gap
9.2.2 Research Question
9.2.3 Importance of the Study
9.2.4 Research Objectives
9.2.5 Scope and Limitation
9.3 Research Methodology
9.3.1 Research Method and Design
9.3.1.1 Sampling Technique
9.3.1.2 Data Collection
9.3.1.3 Data Analysis
9.3.1.4 Ethical Considerations
9.3.2 Research Approach
9.4 Analysis of the Study
9.4.1 Demographic Analysis
9.4.2 Descriptive Analysis
9.5 Results
9.5.1 Hypothesis Testing
9.5.2 Solutions to Research Questions
9.6 Conclusion
9.6.1 Future Scope
9.6.2 Suggestions
References
Chapter 10. AI in Insurance
10.1 Introduction
10.1.1 Converting Paperwork into Digital Data
10.1.2 OCR and Machine Learning
10.2 Customer Data Processing
10.2.1 Online Data and Activity Trackers
10.2.2 Market Making and Operational Efficiency Through AI
10.2.3 Case study: Customer Care Done Right—With Real-Time AI
10.2.3.1 The Challenge
10.2.3.2 Cognizant Approach
10.3 Results
10.3.1 Language Analytics Provide Insights to Customer Satisfaction
10.3.1.1 Key Improvements
10.4 Claim Processing
10.4.1 Applications of AI and ML in the Insurance Sector
10.4.2 Case Study—AI and Automation Improve Insurance Claims Process
10.4.2.1 The Challenge
10.4.2.2 Cognizant Approach
10.4.3 Results
10.4.3.1 Automated Transcription Reduces Call Duration and Costs
10.5 Image Analysis for Damage Insurance
10.5.1 Automobile Damage Inspection
10.5.2 Roof Inspection
10.5.3 Claims Processing with OCR
10.5.4 Questions
References
Chapter 11. AI in Finance
11.1 Introduction
11.1.1 Media Getting Automated Through AI
11.1.2 AI Making Business Easier
11.1.3 AI and ML Services at Our Fingertips
11.1.4 Indian Cos Is Also Racing for AI-Based Services
11.2 Credit Scoring and Loan Analysis
11.2.1 Analysis of Borrower's Creditworthiness
11.2.2 AI for Loan Processing
11.2.3 AI for Service Optimization
11.2.4 Analytics Services for Borrowers
11.2.5 Case Study
11.2.5.1 The Challenge
11.2.5.2 The Solution
11.2.5.3 The Results
11.3 Employee Expense Management
11.3.1 Analysis and Processing of Claims
11.3.2 Cost-Benefit Analysis
11.3.3 Case Study
11.3.3.1 The Challenge
11.3.3.2 The Answer
11.3.4 Results
11.4 Fraud Detection and Anti-Money Laundering
11.4.1 How AI Assists in Identifying Fraud
11.4.2 Case Study
11.5 Personal Financial Advisor
11.6 Risk Assessment and Compliance
11.6.1 Case Study
11.6.1.1 The Challenge
11.6.1.2 The Solution
11.6.1.3 The Result
11.7 Tax Filing and Processing
11.7.1 Applications of AI in Tax Filing and Processing
11.7.2 Case Study
11.7.2.1 The Challenge
11.7.2.2 The Solution
11.7.2.3 The Benefits
11.8 Algorithmic Trading Strategy Performance Improvement
11.8.1 Ability to Predict Future
11.8.2 Risk Management Is Also in Control Using AI and ML
11.8.3 Global Financial Markets Surveillance
11.8.4 Case Study
11.9 Market Intelligence and Data Analytics for Investment
11.9.1 AI and ML for Better Consumer Satisfaction
11.9.2 AI and ML to Analyze the COVID-19 Impact
11.9.3 Creativity and Innovation
11.9.4 Important Questions
References
Chapter 12. AI in Legal
12.1 Automated Report Generation
12.1.1 Applications of ARG
12.1.2 Benefits of ARG
12.1.3 A Legal Case Study From PWC (PricewaterhouseCoopers) Legaltech
12.1.3.1 Select Case Study: Large Pharmaceutical Company Issue
12.1.3.2 Action
12.1.3.3 Impact
12.2 Contract Analysis
12.2.1 Errors in Contracts
12.2.2 Mishandling of Contracts
12.2.3 Contract Analysis Through AI
12.2.4 Key Applications of Contract Analysis Through AI
12.3 Legal Document Review and Research
12.3.1 Document Review
12.3.2 Legal Research
12.3.3 A Legal Case Study From Freshfields Bruckhaus Deringer
12.3.3.1 The Challenge
12.3.3.2 The Solution—Combining Human and AI
12.3.3.3 Teaching Algorithms in Different Languages
12.3.4 The Result
12.3.5 Questions
References
Chapter 13. AI in Supply Chain, Logistics and Manufacturing
13.1 Introduction
13.2 Demand Forecasting
13.2.1 Traditional Versus Machine Learning-Based Forecasting Techniques
13.2.2 Predictive Analytics
13.2.3 Demand Sensing
13.2.4 Popular Deployment of Machine Learning in Demand Forecasting
13.3 Simultaneous Localisation and Mapping
13.3.1 Visual SLAM
13.3.2 LIDAR SLAM
13.3.3 Challenges in SLAM
13.4 LIDAR and RADAR
13.5 Satellite Imagery for Geo-Analytics
13.5.1 Characteristics of Data
13.5.2 Computing Power
13.5.3 Geospatial AI for Global Sustainable Development Goals
13.6 Weather Forecasting
13.7 Human-Robot Collaboration Enhancement
13.7.1 Perceived Safety
13.8 Predictive Maintenance
13.9 Product Life Cycle Management
13.10 Quality Monitoring
13.11 Supply Chain Optimisation
13.11.1 Use Cases of AI in Supply Chain
13.12 Video Surveillance
13.13 Voice/Speech Recognition
References
Chapter 14. Bayesian Machine Learning Approach for Evaluating the Effectiveness of an Order Fulfillment Reengineering Project in the Downstream Oil and Gas Supply Chain
14.1 Introduction
14.2 Theoretical Background
14.2.1 Supply Chain System Integration
14.2.2 Supply Chain Order Fulfillment Process
14.2.3 Process Reengineering for Improving Supply Chain Performance
14.2.4 Poisson Processes for Evaluating the Project Success
14.3 Case Study
14.3.1 The Company
14.3.2 Project Description
14.3.3 Research Model
14.3.3.1 Conceptualization
14.3.3.2 Poisson Processes
14.3.3.3 Model Evaluation
14.4 Results and Discussion
14.5 Conclusion
14.6 Funding
References
Chapter 15. Artificial Intelligence in Sports Industry
15.1 Introduction
15.2 Artificial Intelligence and Machine Learning in Sports Research
15.2.1 The "Moneyball" Case
15.2.2 Areas in Which AI and Machine Learning Have Their Footprints in Sports
15.2.3 Research on AI and ML in Sports
15.2.4 Supervised Learning: Predicting Player Injury
15.3 The Future of AI in Sports
15.4 Challenges in AI Intervention in Sports
15.5 Sportswear Brands Improved Consumer Assignation at Home and in Stores during Covid Pandemic through Visual AI
15.5.1 Visual AI as a Trendsetter in Sports Retailing
15.5.2 Operational Impact of Visual AI
15.5.3 Visual AI Helps to Get Consumers Back Into Brick and Mortar Sports Retail Spaces
15.6 Global Sports Apparel Market Report 2022-2026
15.7 The Nike AI Intervention Case
15.7.1 Nike Customer Engagement and Personalization of the Buying Experience through AI
15.7.2 Market Potential Analysis through the report titled "Global Sports Apparel Market"
15.8 Bogner, Leading Luxurious Sportswear Brand for AI-Powered Business Intelligence
15.9 The Real Moneyball Based on "Moneyball" Movie on Sports
15.9.1 Moneyball Paradigm
15.9.2 How to Apply the Moneyball for Sales Theory to Improve Your Sales Team's Performance
15.10 How Sports Analytics Are Used Today by Teams and Fans
15.10.1 The Rise of Sports Analytics
15.11 Analysis of AI Application in Sports
15.12 Artificial Intelligence in Elite Sports
15.13 Conclusion
References
Chapter 16. Artificial Intelligence Reshaping the Indian Dairy Sector: Better Days Ahead
16.1 Introduction
16.1.1 Growth Drivers in the Indian Dairy Industry
16.2 Milk Production and Procurement
16.3 Technologies for Milk Processing Operations
16.3.1 3D Food Printing Applications
16.4 Cold Chain and Logistics
16.5 Conclusion
References
Chapter 17. Artificial Intelligence and Education
17.1 Introduction
17.2 Artificial Intelligence in Teaching-Learning Process
17.3 Artificial Intelligence and Education Management
17.4 Artificial Intelligence in Academic Fields
17.5 Artificial Intelligence and Curriculum
17.6 Case Study: AIEd in India - A CBSE Initiative for an "AI for All"
17.7 Limitations to the Application of Artificial Intelligence in Education
Reference
Chapter 18. AI in Energy Sector
18.1 Introduction
18.2 Literature Review
18.3 What Were the Traditional Sources of Energy?
18.3.1 Nuclear Energy
18.3.2 Fossil Energy
18.4 What Are the Renewable Sources of Energy?
18.4.1 Solar Energy
18.4.2 Wind Energy
18.4.3 Geothermal Energy
18.5 What Is Artificial Intelligence?
18.6 Types of AI
18.6.1 Narrow Artificial Intelligence
18.6.2 Artificial General Intelligence
18.6.3 Artificial Super Intelligence
18.7 What Is Artificial Intelligence in Energy?
18.8 AI and Primary Challenges Faced by the Modern Energy Sector
18.8.1 Highly Organised
18.8.2 Emission of Carbon
18.8.3 Smooth Switch to Sustainable Energy
18.9 AI's Main Advantages for the Energy Sector
18.9.1 Resource Management
18.9.2 Smart Forecasting
18.9.3 Analytical Modelling for Sustainable Energy
18.9.4 Digitisation of Data
18.9.5 Failure Avoidance
18.10 Applications of AI in the Energy Sector
18.10.1 Consumer Interaction
18.10.2 Detecting Power Theft and Energy Scam
18.10.3 Energy Reserves
18.10.4 Higher Production
18.10.5 Panel Safety
18.10.6 The Power Grid
18.10.7 Reliability and Supervision of the Grid
18.10.8 Statistical Analysis
18.10.9 Power Generation and Planning Optimisation
18.11 Conclusion
References
Chapter 19. Managing Organisational Change Management with AI
19.1 Introduction
19.2 Organisational Change
19.3 McKinsey 7S Model and AI Interventions
19.4 ADKAR Model and AI Interventions
19.5 Kübler Ross' Change Curve Model and AI Interventions
19.6 Limitations of Artificial Intelligence
19.7 Conclusion
References
Other Online resources
Chapter 20. Adoption of Artificial Intelligence in Small and Medium-sized Enterprises: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends
20.1 Introduction
20.2 Methodology
20.2.1 Data Sources and Search Strategies
20.2.2 Quality Assessment
20.2.3 Inclusion and Exclusion Criteria
20.3 Data Analysis and Findings
20.3.1 Attributes of the Papers Covered
20.4 Content Analysis
20.4.1 Co-occurrence Network of Author Keywords
20.4.2 Cluster Analysis
20.4.2.1 Cluster 1: Challenges to AI Adoption among SMEs
20.4.2.2 Cluster 2: Artificial Intelligence and Industry 4.0
20.4.2.3 Cluster 3: AI Adoption in SMEs for Sustainable Production
20.4.2.4 Cluster 4: AI-based Credit Evaluation Systems for SMEs
20.5 Conclusion and Future Research Directions
20.6 Limitations and Future Scope of the Study
References
Chapter 21. AI in Public Sector
21.1 Introduction
21.1.1 Artificial Intelligence
21.1.2 AI in Public Sector
21.1.3 Factors Affecting AI
21.1.4 Solutions of AI in the Public Sector
21.1.5 Challenges in Public Sector
21.2 AI in Public Sector, Around the World
21.2.1 Healthcare
21.2.2 Leading Countries in the Usage of AI for Healthcare
21.2.3 Education
21.2.4 Leading Countries in the Usage of AI in Education Sector
21.2.5 Transportation
21.2.6 Leading Countries in Usage of AI in Transportation Sector
21.2.7 Public Safety
21.2.8 Leading Countries Adapting AI in Public Safety
21.2.9 Government Operations
21.2.10 Leading Countries in Usage of AI in Public Sector
21.3 How AI Is Progressing?
21.4 Merits and Demerits of AI in Public Sector
21.5 Conclusion
References
Index