Book Image

AI and Business Rule Engines for Excel Power Users

By : Paul Browne
Book Image

AI and Business Rule Engines for Excel Power Users

By: Paul Browne

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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Rules-Based AI and Machine Learning AI – Combining the Best of Both

When we introduced rules as the book’s main approach to artificial intelligence (AI) back in Chapter 1, we mentioned machine learning (ML) as a complementary technique. We’ve spent the 10 chapters since then focusing on business rules and integrating them with Excel. So, it’s about time that we returned to the ML side of AI—with a focus on how to combine the two approaches to AI to get the most effective use of AI in your business.

Taking a graphical approach, we’ll explore the thinking behind ML based on sample data from our online chocolate shop. We’ll introduce notebooks, Python, and Azure Machine Learning (Azure ML) and apply these tools to train a simple ML model. We’ll integrate this trained model into a rules-based decision model to improve the recommendation sample we first met in Chapter 4. Finally, since this is only one chapter and very much opening...