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Extending Dynamics 365 Customer Engagement Apps with Low Code

You're reading from   Extending Dynamics 365 Customer Engagement Apps with Low Code Create tailor-made Dynamics 365 CE apps using the powerful low-code capabilities of Power Platform

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Product type Paperback
Published in Jan 2023
Publisher Packt
ISBN-13 9781803232317
Length 342 pages
Edition 1st Edition
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Author (1):
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Nicolás Fernández Nicolás Fernández
Author Profile Icon Nicolás Fernández
Nicolás Fernández
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Introduction of No-Code/Low-Code for Dynamics 365 Customer Engagement
2. Chapter 1: Dynamics 365 Customer Engagement and No-Code/Low-Code FREE CHAPTER 3. Chapter 2: Extending Dynamics 365 Customer Engagement with a No-Code/Low-Code Approach 4. Part 2: Extending Dynamics 365 Customer Engagement Applications
5. Chapter 3: Extending Dynamics 365 Customer Engagement Native Applications 6. Chapter 4: Building Applications with Dynamics 365 Customer Engagement 7. Chapter 5: Dynamics 365 Customer Engagement with Custom Embedded Applications 8. Chapter 6: Extending Your Apps with AI and Mixed Reality 9. Part 3: Building Custom Processes for Dynamics 365 Customer Engagement Applications
10. Chapter 7: Automations in Dynamics 365 Customer Engagement Apps 11. Chapter 8: Working with Data 12. Chapter 9: Integrating Artificial Intelligence into Processes 13. Part 4: No-Code/Low-Code Bots for Dynamics 365 Customer Engagement
14. Chapter 10: Customer-Facing Bots 15. Chapter 11: Enabling Bots to Users 16. Part 5: Working with Advanced Dashboards and Reports with Dynamics 365 Customer Engagement
17. Chapter 12: Reporting Dashboards with Dynamics 365 Customer Engagement Data 18. Chapter 13: Embedded Dashboards and Reports in Dynamics 365 Customer Engagement 19. Index 20. Other Books You May Enjoy

Summary

In this chapter, we looked at how we can begin to introduce AI models as part of our processes to speed up business processes and reduce the burden of manual tasks on users. First, we looked at the AI Builder prediction model. We saw some examples for which each type of prediction could apply, and we went through the steps necessary for setting up our first prediction model. Then, we reviewed the text recognition-based models. We looked at how we could use each one individually and how we could use them together.

Finally, we looked at the models based on processing different types of documents and receipts, delving into how we can train our own document processing model.

In the next chapter, we’ll start working on how and for what purpose we can extend Dynamics 365 Customer Engagement applications with bots. We will start by understanding why bots are becoming increasingly important in a CRM strategy and then see, step by step, how we can go about deploying our...

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