I know. Less exciting than the demo video.
A tool can connect to your warehouse, generate SQL, answer questions in Slack, and still be almost useless if the company hasn’t done the boring data work.
What does “active customer” mean?
Which revenue number should the CEO trust?
Where is the real source of truth for a qualified lead?
Which tables are safe to use, and which ones are historical accidents?
Small details, until an AI agent starts answering with total confidence.
I’ve seen two companies use nearly the same stack and get completely different results.
In one company, the agent becomes genuinely useful.
In the other, it becomes a very expensive way to spread confusion faster.
Same category of tool. Similar data stack. Different outcome.
Why?
Definitions were clearer.
Documentation existed.
Data models had owners.
Edge cases were known.
Someone had actually done the homework.
Wild concept, I know.
That’s why I’m skeptical of “plug-and-play” analytics agents.
The vendor can give you the interface and the harness. But someone still has to teach the system how the business works.
Before trusting one, I’d ask:
“What needs to be true inside our data team before this tool can give answers we’d bet decisions on?”
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