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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Summary

This chapter saw us move from the idea of Decision Models being useful to using Decision Tables and business rules hands-on. Based on a business situation of a chocolate shop, we built a simple product recommendation engine that should give you lots of ideas for your own day-to-day work.

We used many important tools along the way and learned how to model our data using the structured editor, making it clearer what information will be passed in and out of our decision model. We created decision tables, increasing in power and functionality to demonstrate just how scalable a decision-making solution they are. And we looked at the other node types and expressions that help us prepare data for use by decision tables.

Behind these tools, we saw a lot of important concepts – we learned a little bit more about the power of FEEL expressions. We looked at how different HIT policies can make our business rules clearer to understand and avoid gaps. And we saw how to design...