Leverage
Not "AI will replace your analytics team." The opposite bet: the people who already understand your business become dramatically more capable once the mechanical work stops eating their week. We teach them how — on your data, with your tools.
01 The argument
Ask a good analyst what they spend their week on and the answer is rarely analysis. It’s finding the data, cleaning the data, writing the query, fixing the query, reshaping the output, formatting the deck. The genuinely valuable part — knowing which question matters and what the answer implies — is a small slice at the end.
That ratio has changed fundamentally in the last two years, and most businesses haven’t caught up. Used properly, current AI tooling collapses the mechanical portion of analytics work. An analyst who understands the business can now build things that previously required a developer: transformation logic, working data applications, automated analysis pipelines, entire reporting workflows.
The constraint is no longer capability. It’s knowing how. There is a real difference between someone who pastes a question into a chatbot and someone who can direct an AI coding tool through a multi-step data project and verify the result. That difference is teachable, and it is what this service teaches.
Meanwhile your team is almost certainly using these tools already — informally, without guidance, and with no shared view on what data may go where. Training is also how that gets brought under control.
02 Curriculum
Sessions are built from these modules and shaped around what your team actually does. Everything is taught hands-on against your own data — not a tidy demo dataset that behaves itself.
Going from a business question to correct, performant SQL in minutes — including the part most people skip: how to verify that what came back is actually right, and how to recognize the specific ways these tools get joins and aggregation subtly wrong.
Practical technique: how to structure a prompt for analytical work, feed context effectively, iterate toward a result, and use these tools for exploration and hypothesis generation without outsourcing your judgment to them.
Generating and debugging DAX, working through modeling decisions with an AI as a thinking partner, accelerating repetitive report build, and documenting an inherited model you didn’t write and nobody explained.
The largest step change, and the least understood. Directing an agentic coding tool through real multi-step data work — building transformation scripts, automating a recurring process, and doing so without a software engineering background.
Turning a recurring spreadsheet process into a small working tool other people can use. Where this is genuinely a good idea, where it very much isn’t, and how to keep what you build maintainable rather than creating tomorrow’s legacy system.
Scheduled analysis, automated data quality checks, monitoring and narrative summaries. Where an agent genuinely removes recurring work — and the checkpoints that keep a human accountable for anything anyone acts on.
Which data may go into which tool, and why the answer differs between a consumer chatbot and an enterprise deployment. Practical guardrails, a usage policy your team will actually follow, and how to verify output before it reaches a decision.
Arguably the most valuable session. The failure modes specific to data work — plausible-looking joins that double-count, invented column names, silently dropped rows, confident answers to ambiguous questions — and the habits that catch them.
03 Formats
A focused day or half-day with your analysts, finance team or operations group. Hands-on throughout, on your data, with everyone leaving having built something that works.
Best for: getting a whole team moving at once
A sequence of shorter sessions over several weeks, with real work between them. Skills stick considerably better when applied to a live problem before the next session rather than absorbed in one sitting.
Best for: durable capability change
Training delivered alongside a build engagement, so your team learns on the platform we’re building for you and can extend it themselves once we’ve finished.
Best for: avoiding dependency on us
04 What we won’t tell you
It is not a case for reducing headcount. We won’t help you build that argument, and we don’t think the premise holds. The businesses getting real value here are the ones whose analysts started answering harder questions, not the ones who kept the same questions and cut the team.
It is not a slide deck about the future of AI. Nobody needs another one. Every session is hands-on, on your data, producing something that works by the end of it.
It is not tool evangelism. Some of what these tools are marketed as doing, they do not reliably do. We’re specific about where the current generation genuinely changes the economics of analytics work and where it will confidently waste your afternoon.
It is not a substitute for knowing your craft. AI makes a good analyst much faster. It makes a weak analyst faster at being wrong. We teach the verification habits alongside the acceleration, because the second without the first is a liability.
05 Next in the chain
Teaching a team to move fast over data they can't trust just gets you to the wrong answer sooner. Often we do both.
Pipelines and warehousing that get your data out of its systems, into one place, on a schedule.
Explore 02 / TrustBusiness logic, semantic models and metric definitions everyone agrees on.
Explore 03 / DecisionsReporting built around the decisions your business actually makes.
ExploreNext step
We'll tell you honestly which parts of it current tooling can genuinely remove, and which it can't.