Microsoft Dynamics 365 AI for Business Insights [1 ed.] 9781801810944

Table of contents :
Microsoft Dynamics 365 AI for Business Insights
Contributors
About the author
About the reviewer
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Get in touch
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Part 1: Foundations of Dynamics 365 AI
Chapter 1: Introduction and Architectural Overview of Dynamics 365 AI
Why artificial intelligence?
The importance of data-driven insights in business
An overview of Microsoft Dynamics 365 AI for Business Insights
The objectives and structure of the book
Summary
Questions
Answers
Chapter 2: Microsoft Dynamics 365 AI Architecture and Foundations
An overview of the architecture of Microsoft Dynamics 365 AI
Cloud-based architecture
AI technologies integration
Modular components and microservices
Data management and storage
Security and compliance
API and SDKs
Real-time analytics engine
Streamlined user interface
Infrastructure resilience and fault tolerance
Extensibility and future-proofing
The key components and their interactions
Data storage – the bedrock of AI
AI models – the analytical engines
Cognitive services – adding a layer of intelligence
Integration interfaces – the connective tissue
Cross-component collaboration – a symphony of interactions
Business empowerment – the ultimate goal
Scalability and adaptability – designed for growth
Security and compliance across components
Integration considerations and best practices
Data integration – the starting point
Security measures – non-negotiable
Scalability – planning for growth
Performance optimization – getting the most out of your system
Documentation and training – the human element
The iterative nature of integration
Summary
Questions
Answers
Part 2: Implementing Dynamics 365 AI Across Business Functions
Chapter 3: Implementing Dynamics 365 AI for Sales Insights
Leveraging AI for customer segmentation and targeting
Segmentation beyond the surface
Refining targeting strategies
Predictive analysis – the game-changer
Dynamics 365 – a bedrock of quality data
Real-world impact – a clothing brand case study
Predictive lead scoring and opportunity management
Anatomy of predictive lead scoring in Dynamics 365 AI
The transformative nature of predictive scoring in sales
Holistic opportunity management with Dynamics 365 AI
Deep dive into predictive analysis and its implications
An illustration of predictive lead scoring
Personalization and recommendation engines for sales effectiveness
Data-driven personalization in Dynamics 365 AI
Recommendation engines – beyond the obvious
Feedback loops and iterative refinement
Personalization in action – a real-world glimpse
Examples
Example 1 – ElevateApparel’s customer segmentation triumph
Example 2 – ProTech Solutions and the predictive power
Example 3 – NovelReads’ personalized book journey
Limitations and pitfalls of using AI for sales
Summary
Questions
Answers
Chapter 4: Driving Customer Service Excellence with Dynamics 365 AI
Enhancing customer experience with virtual agents and chatbots
The mechanics of continuous learning
Feedback loops and data analysis
Example of adaptation in action
Training with synthetic data
Real-time performance adjustments
Evolving with consumer trends
Integration with human feedback
AI-powered sentiment analysis and customer sentiment tracking
Technical aspects of sentiment analysis
ML for enhanced sentiment detection
Real-time sentiment tracking and response adaptation
Predictive analytics in sentiment analysis
Sentiment analysis for personalized marketing
Data-driven strategy adjustments
Challenges and ethical and security considerations
Intelligent routing and case management for efficient support
The mechanics of intelligent routing
Enhanced efficiency with AI algorithms
Case management and automated resolution
Predictive analysis in case prioritization
Integration with CRM systems
Real-time adjustments for peak efficiency
Challenges in implementation
Real-world examples of AI-driven customer service enhancements
Example 1 – Global bank incorporates AI for efficient customer query handling
Example 2 – E-commerce platform utilizes AI for personalized customer support
Example 3 – Telecom giant implements AI for streamlined case management
Summary
Questions
Answers
Chapter 5: Marketing Optimization with Dynamics 365 AI
AI-driven customer segmentation and campaign targeting
Advanced customer segmentation
Machine learning and predictive analytics
Personalization at scale
Real-time campaign adjustments
Seamless omnichannel marketing integration
Ethical considerations in data handling
Personalized recommendations and cross-selling opportunities
Advanced personalization techniques
Deep learning for enhanced customer insights
Real-time recommendation engines
Cross-selling strategies powered by AI
Omnichannel personalization
Utilizing customer feedback for continuous improvement
Data-driven insights for marketing campaigns
Ethical and responsible AI practices
Social media sentiment analysis and brand perception insights
Harnessing social media data
Sentiment analysis and emotional intelligence
Real-time brand perception tracking
Predictive analytics for proactive brand management
Incorporating customer feedback into strategy
Case study – Retail brand leverages social sentiment analysis
Real-world examples and best practices in marketing insights
Example 1 – Hyper-personalized campaigns by a fashion e-commerce platform
Example 2 – Optimized patient outreach by a healthcare provider network
Example 3 – Market expansion strategy for a SaaS company
Summary
Questions
Answers
Chapter 6: Financial Analytics with Dynamics 365 AI
Enhanced financial forecasting and budgeting with AI
Technical sophistication in predictive analytics
Automation in budgeting processes
Dynamic and adaptive financial planning
Scenario planning and risk assessment
Business impacts and considerations
Enhanced fraud detection and prevention using advanced analytics with Dynamics 365 AI
Employing a multifaceted analytical approach for detection
Machine learning for dynamic and adaptive fraud detection
Seamless integration with organizational data systems
Real-time detection and automated intervention
Navigating ethical terrain and ensuring compliance
Revolutionizing risk assessment and mitigation strategies
Enhanced risk identification through deep data analysis
Detailed risk analysis and quantification
Strategic mitigation with AI insights
Adaptive monitoring for ongoing risk management
Ethical and regulatory adherence in AI-driven risk management
Dynamics 365 AI – transforming financial operations
Case study 1 – forecasting accuracy in a multinational corporation
Case study 2 – banking on AI to combat fraud
Case study 3 – risk management reinvented for an investment firm
Summary
Questions
Answers
Part 3: Advanced Applications and Future Directions
Chapter 7: Leveraging Generative AI in Dynamics 365
The mechanism behind generative AI – An in-depth technical exploration
Advanced neural networks in GANs
Training dynamics and computational aspects
Generative AI in text and language processing
Technical sophistication in language applications
Challenges and considerations in implementation
Azure Open AI Service: An in-depth technical exploration
Foundational integration with Microsoft Azure
Operational mechanics of Azure Open AI Service
Enhancing AI performance in the cloud
Security, compliance, and ethical considerations
Integrating language models and ChatGPT with Dynamics 365 AI
Detailed integration process
Architectural foundations of integration
Enhancing Dynamics 365 with AI capabilities
Addressing implementation challenges
Future enhancements and evolutions
Real-world use cases and implementation examples of integrating language models and ChatGPT with Dynamics 365 AI
Use case 1 – Multinational retail chain enhances customer experience
Use case 2 – Finance consulting firm leverages AI for market analysis
Use case 3 – Global corporation streamlines HR operations
Summary
Questions
Answers
Chapter 8: Harnessing MS Copilot for Enhanced Business Insights
Overview of MS Copilot and its comprehensive features
Advanced data processing and analysis
The integration of cutting-edge AI technologies
Enhancing business intelligence
User experience and interface design
Real-time interaction and automated customer support
Integrating MS Copilot with Dynamics 365 AI
Harmonizing advanced technologies
Enhancing Dynamics 365 with AI
Best practices and real-world integration scenarios
Transforming business operations and development
Leveraging MS Copilot for code generation and optimization in Dynamics 365 AI
Case studies in harnessing MS Copilot for enhanced business insights
Case study 1 – revolutionizing retail with personalized customer experiences
Case study 2 – enhancing healthcare services with predictive analytics
Case study 3 – streamlining manufacturing with AI-driven supply chain optimization
Case study 4 – financial services’ strategic decision-making with market analytics
Summary
Questions
Answers
Chapter 9: “Virtual Agent for Customer Service” in the Context of MS Copilot and Microsoft Dynamics
Implementing virtual agents for automated customer support with MS Copilot
Advanced technological infrastructure of virtual agents
Seamless integration with customer support systems
Diverse capabilities and functionalities
Enhancing operational efficiency and customer experience
Implementation best practices
Ongoing monitoring and enhancement
Integration of virtual agents with customer service processes in Dynamics 365
Business considerations for effective deployment
Strategic approaches for effective integration
Advanced customer interaction and support capabilities
Focused training and customization for optimal functionality
Addressing challenges in integration
Evaluating impact and effectiveness
Case studies and success stories in virtual agent implementation
Case study 1 – Retail giant enhances customer experience with AI virtual agents
Case study 2 – Financial services firm boosts efficiency with AI virtual agents
Case study 3 – Healthcare provider improves patient support with virtual agents
Summary
Questions
Answers
Chapter 10: Fraud Protection with Dynamics 365 AI
AI-driven fraud detection and prevention strategies
Machine learning for pattern recognition
Natural language processing for fraudulent claims detection
Predictive analytics for future threat identification
Continuous learning and adaptation
Integration challenges and considerations
Identifying anomalies and patterns using advanced analytics
Sophisticated data analysis tools and techniques
Extending with Copilot Studio
Diagnostic analytics
Dynamics 365 supply chain management’s advanced AI-powered demand forecasting
Microsoft Intune Advanced Analytics
Machine learning for enhanced detection
Real-time analytics for immediate action
Incorporating external insights
Navigating challenges with precision
Leveraging Dynamics 365 AI for real-time fraud monitoring and mitigation
Real-time fraud monitoring capabilities
Automated alerts and immediate mitigation
Adaptive learning for evolving threats
Case studies and success stories in fraud protection insights
Case study 1 – Global e-commerce platform enhances security with Dynamics 365 AI
Case study 2 – Financial institution prevents loan application fraud
Case study 3 – Healthcare provider targets insurance fraud with Dynamics 365 AI
Summary
Questions
Answers
Part 4: Looking Ahead
Chapter 11: Future Trends and Developments in Dynamics 365 AI
Emerging trends in AI for business insights
AI and machine learning sophistication
Predictive analytics and forecasting
Automated AI (AutoML) and no-code AI solutions
AI-driven NLP
Integration of AI across business processes
Ethical AI and bias mitigation
Edge AI for real-time insights
Microsoft’s roadmap for Dynamics 365 AI – anticipated developments and features
Enhanced AI models and analytics
Seamless integration across the Dynamics 365 suite
Expanded no-code AI capabilities
Advanced NLP for customer insights
Real-time AI processing at the edge
Ethical AI and governance
AI-powered automation and robotic process automation (RPA) enhancements
Industry-specific AI solutions
Exploring advancements in AI technologies and their implications for Dynamics 365 AI
Federated learning – a new paradigm in data privacy and AI
AI and the Internet of Things (IoT) – bridging the physical and digital worlds
Quantum computing – supercharging AI’s analytical capabilities
Explainable AI (XAI) – enhancing transparency and trust
Generative pre-trained transformers (GPT) and advanced NLP – revolutionizing customer interactions
AI ethics and governance – shaping a responsible future
Summary
Questions
Answers
Index
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