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According to what VK noted under his forum questions, “Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone..” AI is created by humans who are prone to error. We must always remember this and be diligent to make sure AI will make the best decisions. “Making sure” will ALWAYS to be and, I believe, will forever be, a human responsibility. I can’t ever imagine anyone shirking their responsibility and pointing at the AI solution and saying “It’s the AI’s fault that we lost revenue”. Yes. It might have been that we trusted the AI agent to make the decision but ONLY after we allowed it to make that decision. So, the real accountability still falls back to a human. Knowing that AI is prone to make errors, just as humans have done to mitigate making our own errors, we created guardrails to increase proper decision making and better outcomes – ergo, Business Excellence. Think of AI as another person. But now you are responsible for the decisions and actions of that person. It will need oversight, accountability, and transparency to make sure AI is making the right decisions on our behalf. Here are some of the elements that I think could be included in a governance framework to ensure responsible, high-impact use of AI in a process-driven organization. Creating a governance team or committee to oversee all AI solutions. This team would comprise people from IT, the business, legal, risk management and defining each role and responsibility throughout the AI development, deployment and maintenance. For transparency and accountability, conducting regular impact assessments to identify potential risks, biases and consequences of AI-driven decision. Also, implementing techniques that can provide insights into the how AI is making its decisions, such as feature attribution or model interpretability methods. Lastly, performing audit trails that let us see the data inputs, processing and outputs the AI used to make its decision. For agility and control, using agile development methodologies to allow for rapid iterations and deployment. Using change management to capture the all the changes made throughout the development which can easily be reviewed, tested, and validated. Lastly, establish access controls to prevent unauthorized changes to the AI system or data.