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Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets.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

Sharing and Deploying Decision Models Using OpenShift and GitHub

In the previous two chapters, we created decision models and business rules in the KIE Sandbox. While the Rule runner screen in the sandbox is great for rapid, iterative development, all the editing is local and we can’t share the running decision models with colleagues. Since we’re using KIE extended services, models can only be accessed from our laptop. That’s not great if we want to collaborate with colleagues or deploy our models into production. We’ll remedy that in this chapter by covering the following:

  • How deploying our models to the cloud makes them easier to use
  • OpenShift as Red Hat’s cloud offering
  • Important issues on data privacy that you will be glad you started thinking of now
  • Saving and sharing your decision models in GitHub, the most widely used collaboration tool

By the end of this chapter, we will have our decision models backed up to a secure...