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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Business rules as preparation for Machine Learning

In Chapter 1, we suggested combining the two AI approaches (rules and ML) to build a self-driving car. The ML approach is great for fuzzier requirements (to identify whether it is a dog or a child standing in the road). Rules are better for requirements we can state clearly and that must always be implemented (for example, swerve the car to avoid a child but do not swerve for an animal as it risks a more serious accident).

Your organization should be able to follow a similar approach—there are rules that can be clearly written by a human expert (for example, buyers must have a 20% deposit for their home loan). And there are experiences that are harder to express—a senior bank official might have a feeling that a loan application is fraudulent and need further investigation, but might struggle to explain exactly why. In our business, we are likely to need both rules and Machine learning approaches to mimic both these...