Most “AI analytics” pitches answer the question that’s easy to demo, not the question that’s hard to decide. We work the other way around — start with the decision the business needs to make, then build the analytics that informs it. Sometimes the answer is a model. Sometimes it’s a dashboard. Sometimes it’s a one-page briefing that says “you don’t have the data to answer this yet, here’s what to collect.”
What this looks like in practice
We start by asking what decision changes if the answer is X versus Y. If the decision is the same either way, the analysis isn’t worth running — and that’s an honest finding to deliver in week one. If the decision is sensitive to the answer, we build the smallest pipeline that delivers it, validate the result against ground truth, and produce a briefing in language a non-technical stakeholder can act on.
For a deeper method on how we design these workflows — starting from a target output and working backwards — see Build AI Workflows in 5 Steps, the provider-agnostic Jupyter lesson we put on a public shelf.
Who this fits (and who it doesn’t)
Fits: small businesses with a real operational decision (pricing, churn, inventory, hiring) and the data to inform it but no analytics function. Research labs that need productionizable pipelines around a model. Teams that have been told “AI will help” and need someone to figure out whether that’s true.
Doesn’t fit: companies looking to “implement AI” with no specific decision in mind. We can’t ship that responsibly.
The first conversation
We’ll ask what decision the analytics is supposed to support, what’s at stake if it’s wrong, and what data exists today. Within an hour we usually know whether this is a model, a dashboard, or a “no, here’s what to collect first.”