An AI agent will hand you an analysis that is confidently, beautifully wrong

An AI agent will hand you an analysis that is confidently, beautifully wrong, and you will not be able to tell.

The agent renders a clean chart. The numbers are plausible. The narration is confident. Everything about the output says “trust me,” and most of the time the methodology underneath is fine. But when it’s not, nothing on the surface tells you. Looking correct and being correct are two different things in data work, and the gap between them is invisible right up until a real decision falls through it.

Here’s how it actually goes wrong, in my experience. The bad analysis doesn’t blow up in the meeting. It sails through. Then weeks later the campaign underperforms against what the model promised, and someone finally pulls the thread. They find a join that quietly fanned out rows and triple-counted spend, or an attribution window double-counting conversions so every channel looks like a winner. A real analyst catches those on sight. An agent sails past them. So does a smart person who isn’t an analyst, because the output looked completely fine.

Simon Willison draws a sharp line between vibe coding and agentic engineering. The same line runs straight through analytics. Letting the agent rip is genuinely great for a throwaway chart to get a feel for the data, and I do it constantly. It is not enough for anything a budget rides on. A prototype is not the same as something reproducible.

That doesn’t mean you shouldn’t use AI in your analytics. Quite the opposite. AI makes a good analyst dramatically faster and more valuable. What it doesn’t do is make the analyst optional, and acceleration without expertise is exactly where the risk piles up. The expert is what makes the speed safe.

So, honest question: the last agent-generated analysis you made a real decision on, did anyone actually check the assumptions underneath it, or did it just look right?

#DataAnalytics #PerformanceMarketing #AI #MarketingAnalytics

First comment: One cheap habit that catches a lot: before trusting any agent-built analysis, ask it to show the row count at each step. Rows appearing or vanishing between a join and the final table is where a surprising share of “confidently wrong” lives.