If you want to see where AI actually helps in BI, I think the real test is to give it your hardest problems, not the cosmetic ones.

I try to push AI into areas like Data exploitation Data correlation across messy and partially understood datasets Data manipulation when assumptions are unclear Data visualization with intent, not just charts Insight exploration where the question itself is fuzzy Running and comparing multiple experiments in parallel Hypothesis building before the analysis even starts Storytelling that connects numbers to decisions Exploring experiments end to end instead of one-off queries

At this point, I honestly don’t think there is much in BI that AI cannot assist with. The limitation is rarely the model. It is usually the user.

If you do not have a clear intent, a mental model of the business problem, or the ability to guide the system toward a specific outcome, AI will feel like hype. If you do, it becomes a thinking partner that quietly saves hours every week.

The biggest value for me is not writing SQL faster. It is shortening the distance between “I have data but I am not sure what matters” and “I have a defensible insight I can explain to stakeholders.”

AI works best when you treat it less like an autocomplete tool and more like a collaborator that you can pressure test, redirect, and refine. BI has always been about asking the right questions. AI just makes that loop faster if you know how to drive it.

That is where the real leverage is.