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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

What Next? A Look inside Neural Networks, Enterprise Projects, Advanced Rules, and the Rule Engine

Since this is the final chapter of the book, we’re going to use it to extend some tools and concepts we met in the first 11 chapters. We also want to answer the question what’s next?, so we’ll focus on areas that you can explore after you finish this book. We’ll cover these areas in the following order:

  • We will start by evolving the notebook we used in the previous chapter, demonstrating decision trees as another classification algorithm.
  • Decision trees give us more explainability. We will explore this in more detail and show how they can be converted into the business rules and decision tables we first met in Chapter 4.
  • Neural networks are among the most complex classifiers that you’re likely to encounter. We will give you a high-level introduction via Excel, then show how you can generate them in Python.
  • Neural networks are powerful...