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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Graphical introduction to Machine Learning

Don’t be scared of math—it’s just numbers. And since every number can be plotted on a graph, we can think of machine learning as patterns and pictures and teaching the computer to recognize those pictures.

While that approach is simplistic, the truth is that nobody is smart enough to carry out all the math needed for machine learning. Even if you fully understood the theory behind it, it is impossible for the human brain to think in the many trillions (yes—trillions) of dimensions of data points used in some ML projects. And even if we got that far, the sheer volume of calculations needed would take longer than a human lifetime to work through.

Happily, the machine in machine learning means there are tools available to help us. There are prebuilt toolkits (libraries) available that we’ll use to train our models. And while it does help to understand the strengths of each toolkit, you can get a long way...