Book Image

AI and Business Rule Engines for Excel Power Users

By : Paul Browne (GBP), PORCELLI
Book Image

AI and Business Rule Engines for Excel Power Users

By: Paul Browne (GBP), PORCELLI

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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Summary

This chapter set out to build on the previous 11 – extending some of the ideas that we met earlier in the book and giving you signposts for where to go next.

In this chapter, we swapped out the previous machine learning algorithm for decision trees and saw how this allowed us to explain what is going on within our decision model. Lack of explainability became an issue when we introduced neural networks as more powerful classifiers, but we solved the problem using cutting-edge TrustyAI tools.

We introduced three ways to deploy combined machine learning and rules-based AI models at a high level via Business Central, and in more depth using KIE and Kogito samples, and again using Python and Power Automate workflows. We introduced another method using Red Piranha as a template enterprise solution to build on, including its ability to read entire Excel spreadsheets and apply rules to them.

We looked at the more advanced DRL rule format and saw that it gave us additional...