Business Analysis and AI

I did not understand. That phrase is encountered less and less in today's AI powered era. You see, everyone "understands" with the summary provided by AI. Everyone "knows" with the knowledge support of a great LLM that taps on the knowledge of millions just to show something. And given time, you may even write a whole essay for this thing that you know nothing about. People fear not understanding something. They believe that not understanding will make them weak, unable to negotiate properly against someone who does. But not understanding is not something to fear. It is the first step towards true knowledge. You do not know something. You do know other things. And must build on these, while acknowledging that some things are beyond what you currently hold. Knowledge does not come for free. And it certainly does not come in the form of a Gemini or GPT summary an hour before you get into that meeting. You must spend time and effort to know something. Try...

The AI systems we use are built on data. Data that are derived from the world we live in. As Business Analysts (BAs) working as trusted advisors towards a better world, our job is still the same in its core. We still work with people trying to understand needs and the best ways to satisfy them. However, with AI, a new dimension is added to this work: We must now also safeguard the way AI systems are trained and the way they produce outputs, so that these outputs are aligned with the better world we all strive for. For example, a new banking screening process should be fair to all. This was true before the advent of AI and it is still crucial in the AI era we live in. In both cases the advisor to the customer should pinpoint potential areas that could generate bias and eliminate them. The BA should work to clarify and establish the criteria used and how their application will result in better decisions. If in the past this was a set of COBOL programs in zOS or if in the future...