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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Rule matching and HIT Policies

For the decision table we saw in Figure 4.23, a US-based customer, with a customer number of less than 10,000 would match with three rules (Silk Tray, Peanut Crunch, and the default Milk Chocolate). But how do we decide which rule we should use? To do that, we need to talk again about HIT Policies.

In our previous example, we set the HIT policy to First (in the top left corner of the decision table) as it was the easiest to understand. When we match with two rules, Silk Tray comes first, so it’s the one that is chosen.

That’s fine when we have only a couple of rules, but when we have hundreds of rules on a table, it becomes harder to see what is going on. Nudging a rule up or down the decision table could bring big changes to the behavior of the decision model. It also doesn’t leverage the full power of the rule engine. For these reasons, the HIT Policy of First is controversial and doesn’t form part of the OMG Decision...