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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

DMN and FEEL expressions – extending what you know

As we worked through Chapters 3 and 4, we covered many of the key points of the FEEL expression language:

  • We saw that FEEL allows you to write boxed expressions (like what you type into the Excel formula box). There are many different places that you can use FEEL expressions – from decision nodes to lists to decision tables.
  • One sort of FEEL expression was text and string manipulation – we used it to create a Hello [your name] example. If you’ve read the reference docs, you’ll have seen that most text functions in Excel have an equivalent in FEEL. This includes converting strings into uppercase or lowercase, extracting text from the beginning, middle, or end of the text, replacing values, testing whether the text starts or ends with specific values, as well as splitting or combining text.
  • We used variables as placeholders for different values. Looking at the reference docs, you can...