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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Deploying the two AIs together (ML and rules)

ML is great, but this is a book mainly about AI business rules. Our first step to combine the two is to open and edit our PMML model in Azure, as in Figure 11.17. This PMML model was generated in the last step of our Python notebook. You may need to click on the Refresh button in Azure ML to see this newly generated model:

Figure 11.17 – PMML generated by the ML script

As you can see, PMML is an XML format that is largely human-readable. It also has the advantage of being an industry standard, so even though sklearn and a Python notebook generated the model, we are able to import and execute the model in KIE. In general, the interoperability works smoothly—but a bit like assembling a cabinet from Ikea, sometimes a few gentle knocks from a mallet are needed to get everything into place.

In our case, there are two edits needed to the PMML file so that Kogito can work with it:

  • On the second...