AI didn't create your data problem. It just made it impossible to ignore.

I was at a conference yesterday. AI dominated every conversation — keynotes, panels, hallway talk. The energy was real.

But a pattern kept surfacing beneath the enthusiasm.

Everyone agreed: it's never been easier to ask questions of your data. What's harder to admit is what happens when you do.

Ask three people in your organization what "customer" means. Or "revenue." Or "active user." You may get three different answers. Not because people are careless. Because no one ever had to agree before.

For years, analysts have been quietly translating between business questions and data reality. They knew which table to trust. They knew when a number needed a footnote. They held the institutional knowledge that made the data make sense.

That work was invisible. It happened before the dashboard loaded, before the report landed in your inbox. It was the human layer between raw data and a confident decision.

AI removed that buffer.

Now anyone can walk up to their data and ask a question in plain language. That's genuinely exciting. But it also means the gaps analysts were bridging — the undefined terms, the conflicting definitions, the metrics that mean different things in different departments — are now everyone's problem.

New technology doesn't always solve old problems. Sometimes it just shines a brighter light on them.

So here's the uncomfortable question worth sitting with: if your organization scaled AI access tomorrow, would the answers be trustworthy? Or would you get faster, more confident versions of the same confusion you already have?

I don't think there's a clean answer. But I think the organizations that will get the most from AI are the ones willing to ask the question honestly.