Microsoft Dynamics 365 AI for Business Insights Transform Your Business Processes with the Practical Implementation of Dynamics 365 AI Modules
Streamline your business operations by leveraging AI across key functions using practical examples and insightful case studies Key Features Uncover the practical applications of Dynamics 365 AI across sales, customer service, marketing, and finance Explore Dynamics 365's integration with advanc...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009810645106719 |
Tabla de Contenidos:
- Cover
- Title page
- Copyright and credits
- Contributors
- Table of Contents
- Preface
- 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.
