Book Image

AI and Business Rule Engines for Excel Power Users

By : Paul Browne
Book Image

AI and Business Rule Engines for Excel Power Users

By: Paul Browne

Overview of this book

Microsoft Excel is widely adopted across diverse industries, but Excel Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets. They need a better enterprise-level solution, and this book introduces Business rules combined with the power of AI to tackle the limitations of Excel. This guide will give you a roadmap to link KIE (an industry-standard open-source application) to Microsoft’s business process automation tools, such as Power Automate, Power Query, Office Script, Forms, VBA, Script Lab, and GitHub. You’ll dive into the graphical Decision Modeling standard including decision tables, FEEL expressions, and advanced business rule editing and testing. By the end of the book, you’ll be able to share your business knowledge as graphical models, deploy and execute these models in the cloud (with Azure and OpenShift), link them back to Excel, and then execute them as an end-to-end solution removing human intervention. You’ll be equipped to solve your Excel queries and start using the next generation of Microsoft Office tools.
Table of Contents (22 chapters)
Free Chapter
1
Part 1:The Problem with Excel, and Why Rule-Based AI Can Be the Solution
5
Part 2: Writing Business Rules and Decision Models – with Real-Life Examples
9
Part 3: Extending Excel, Decision Models, and Business Process Automation into a Complete Enterprise Solution
13
Part 4: Next Steps in AI, Machine Learning, and Rule Engines
Appendix A - Introduction to Visual Basic for Applications

Part 4: Next Steps in AI, Machine Learning, and Rule Engines

The final section of the book aims to leave you with a good platform to learn more about AI, machine learning, and rule engines.

This section includes the following chapters:

  • Chapter 10, Scaling Rules in Business Central with Docker and the Cloud, demonstrates the power of the KIE and Kogito decision-making tools, leveraging containers to increase our deployment options and editing capability.
  • Chapter 11, Rule-Based AI and Machine Learning AI – Combining the Best of Both, introduces the other main part of AI and shows how we can use the tools in KIE and Azure ML Studio to deploy both machine learning and rule-based decision models alongside each other.
  • Chapter 12, What Next? A Look inside Neural Networks, Enterprise Projects, Advanced Rules, and the Rule Engine, expands on the previous chapters and looks at key areas for future learning with practical first steps in neural networks, enterprise Java...