Tag: analytics
A report and a semantic model are not the same thing, and your AI agent only cares about one of them
Most people treat a Power BI report and its semantic model as one object. They are two files doing two jobs. When you point an AI agent at your data, the report is the cheap half and the semantic model is the part that took three years to get right.
BI only ever saw half your company. AI can see the other half
Business intelligence was always the quantitative side: rows, numbers, things that fit in a column. The qualitative half, the calls and emails and tickets where the why actually lives, was invisible to it. That half is most of your data, and it is where AI adds value BI never could.
One-time question or a permanent dashboard? AI just changed the answer
Every BI team has quietly run the same triage for years: is this worth a dashboard, or is it a one-off? Building a dashboard was the only durable option, so the long tail of one-time questions mostly went unasked. AI collapses the cost of the one-off, and that reshapes the whole portfolio.
The hard part of analytics AI is not the answer, it is figuring out the question
Everyone obsesses over whether the model reasons well. The real failure in AI over business data happens earlier, at the moment the agent decides what you meant. A confident answer to the wrong question is worse than no answer at all.
Your old dashboards are the answer key for your new AI
Teams building analytics AI keep starting from a blank page. Meanwhile the most validated business logic they own is sitting in the dashboards they already shipped. Those reports are years of distilled definitions and a ready-made test set. Mine them.