Your company bought AI licenses. Leadership wants to know what they returned. “People seem to like it” is not an answer a board accepts — and the honest answer is buried in usage logs, license counts, and workflows nobody has instrumented.
This is a measurement problem, and measurement is data engineering. We build it:
- Usage telemetry across teams and tools — who is actually using which AI tools, how often, and for what
- The adoption funnel — licensed → active → habitual, broken down by team, so you can see where adoption stalls and why
- Pre-rollout baselines — the before-picture, captured properly, so “time saved” is a measurement rather than a testimonial
- Task-level impact — time saved per workflow, output acceptance rates, and rework rates on AI-assisted work
- Cost per outcome — license and inference spend tied to the outcomes it produced, not just the seats provisioned
- An executive scorecard — one view leadership checks monthly, built to survive a CFO’s questions
The deliverable
A live adoption and ROI dashboard on your own data platform, plus the instrumentation feeding it — so the answer to “what did AI return this quarter?” is a link, not a research project.
Engagement shape
Typically three to six weeks to the first scorecard. We inventory your AI tooling and spend, wire up the telemetry you already have and add what’s missing, establish baselines, and stand up the scorecard. What it reveals — where AI is underperforming, and why — usually sets the agenda for harness and quality work on the features themselves.
Ready to talk it through?
Tell us where your data hurts — we'll tell you honestly whether we can help.
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