Our firm is looking to move beyond basic Robotic Process Automation into Intelligent Automation. We want to incorporate Machine Learning models for document processing, but I'm worried about the 'black box' nature of AI. What are the best practices for ensuring data governance while scaling these AI-enhanced bots?
3 answers
The transition from RPA to Intelligent Automation requires a robust "Human-in-the-Loop" (HITL) framework. You shouldn't just let the ML model make final decisions on sensitive data immediately. Start by using the AI to provide confidence scores. If a score is below 90%, the bot should route the task to a human validator. This builds an audit trail and helps retrain the model. For data governance, ensure your training sets are scrubbed of PII and that you are using explainable AI (XAI) tools to document why certain automated decisions were made during the processing phase.
Are you more concerned about the technical latency of adding ML layers, or is the primary hurdle getting your compliance team to sign off on the non-deterministic outputs?
Focus on "Process Mining" first. It helps you identify exactly which parts of your RPA workflow are failing due to unstructured data, making the AI's job much more specific and manageable.
Agreed, Laura. Process mining takes the guesswork out of the equation. It's much easier to justify the cost of Machine Learning when you have hard data showing exactly where the bottlenecks in your current bots are.
To address the compliance aspect you mentioned, Kevin, the hurdle is usually the "non-deterministic" part. I’ve found that creating a dedicated "AI Governance Sandbox" helps. We run the bots in a shadow environment for six weeks and compare the AI's output against our legacy RPA rules. Once the variance is under 2%, the compliance team usually feels much more comfortable with the production rollout, especially if we maintain strict versioning on our models.