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Microsoft Dynamics 365 AI for Business Insights

You're reading from  Microsoft Dynamics 365 AI for Business Insights

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781801810944
Pages 178 pages
Edition 1st Edition
Languages
Author (1):
Dmitry Shargorodsky Dmitry Shargorodsky
Profile icon Dmitry Shargorodsky

Table of Contents (18) Chapters

Preface 1. Part 1: Foundations of Dynamics 365 AI
2. Chapter 1: Introduction and Architectural Overview of Dynamics 365 AI 3. Chapter 2: Microsoft Dynamics 365 AI Architecture and Foundations 4. Part 2: Implementing Dynamics 365 AI Across Business Functions
5. Chapter 3: Implementing Dynamics 365 AI for Sales Insights 6. Chapter 4: Driving Customer Service Excellence with Dynamics 365 AI 7. Chapter 5: Marketing Optimization with Dynamics 365 AI 8. Chapter 6: Financial Analytics with Dynamics 365 AI 9. Part 3: Advanced Applications and Future Directions
10. Chapter 7: Leveraging Generative AI in Dynamics 365 11. Chapter 8: Harnessing MS Copilot for Enhanced Business Insights 12. Chapter 9: “Virtual Agent for Customer Service” in the Context of MS Copilot and Microsoft Dynamics 13. Chapter 10: Fraud Protection with Dynamics 365 AI 14. Part 4: Looking Ahead
15. Chapter 11: Future Trends and Developments in Dynamics 365 AI 16. Index 17. Other Books You May Enjoy

Enhanced financial forecasting and budgeting with AI

The advent of AI in financial forecasting and budgeting is creating a paradigm shift in how businesses approach their financial strategies and operations. With Dynamics 365 AI, companies are not only equipped to handle vast amounts of financial data but are also empowered to draw sophisticated, actionable insights from it. Let’s delve into the technicalities of AI’s impact on financial forecasting and budgeting and the consequent business implications.

Technical sophistication in predictive analytics

Dynamics 365 AI incorporates complex machine learning algorithms and statistical models to conduct predictive analytics, going far beyond traditional linear regressions. These models include time series analysis, neural networks, and ensemble methods that can handle non-linear patterns, seasonality, and volatility in financial data. For instance, by applying these advanced techniques, businesses can anticipate cash...

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