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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Further reading

This chapter was written to give you the confidence and the desire to know more about machine learning. We introduced several key topics that your interests may lead you toward, from learning more about Azure and Azure ML to taking a deeper dive into Python notebooks and the different algorithms implemented in them. All of these topics have Packt Publishing books available on them.

At this point, it is also worth (re)viewing the Kogito and Drools documentation with a focus on the AI tools and integration, since much of the information there will now make a lot more sense to you, as well as giving practical steps on how to deploy combined rules and ML decision services.

One non-Packt Publishing book that I highly recommend is Mathematics for Machine Learning. It’s available as a free/open book from https://mml-book.github.io/. While it is very clearly written and accessible, it is still a math book. So, while it is a fascinating read and will enhance your...