Embedding Machine Learning into RPA Process

Something we did, but with BPM models and process flow.   Makes sense because you can better understand the context involved.  Process models, even simple visualizations, can help sell the model, get useful data, and promote the contextual design and value.  Rules are understandable, but algorithms are usually not to decision makers.

Small ML is the next big leap in RPA
Instead of doing big ML projects, embed ML into your day-to-day RPA work and be amazed.   By Eljas Linna in TowardsDataScience

The boom in robotic process automation (RPA) over the past few years has made it pretty clear that business processes in nearly every industry have an endless amount of bottlenecks to be resolved and efficiency improvements to be gained. Years before the full surge of RPA, McKinsey already estimated the annual impact of knowledge work automation to be around $6 trillion in 2025.

Having followed the evolution of RPA from python scripts towards generalized platforms, I’ve witnessed quite a transformation. The tools and libraries available in RPA have improved over time, each iteration widening the variety of processes that can be automated and improving the overall automation rates further. I believe the addition of machine learning (ML) in the everyday toolbox of RPA developers is the next huge leap in the scope and effectiveness of process automation. And I am not alone. But there’s a catch. It will look very different from what all the hype would lead you to believe.

Why even care about machine learning?
Imagine RPA without if-else logic or variables. You could only automate simple and completely static click-through processes. As we gradually add in some variables and logic, we can start automating more complex and impactful processes. The more complex the process you want to automate, the more logic rules you need to add, and the more edge cases you need to consider. The burden on the RPA developer’s rule system grows exponentially. See where I’m going with this?  ... "