Most teams don’t need a moonshot to benefit from AI. They need a disciplined first quarter that turns curiosity into a measurable win. Here’s the approach we use with clients.
Weeks 1–2: Find the real problems
Start with outcomes, not tools. Interview the people doing the work and look for tasks that are frequent, rules-light, and text- or data-heavy. Rank candidate use cases by value and feasibility.
Weeks 3–6: Prove it small
Pick one use case and build the smallest thing that could work. The goal is evidence, not a platform:
- Define what “good” looks like and how you’ll measure it.
- Build a thin prototype against real data.
- Put it in front of a handful of real users.
Weeks 7–12: Harden and integrate
If the prototype earns its keep, make it dependable: add evaluation, monitoring, and guardrails, and integrate it into the tools people already use. Only then consider expanding to the next use case.
The throughline
Ship narrow, measure honestly, and expand from proof. AI projects fail when they start too big — and compound when they start small and disciplined.
Want help mapping your first 90 days? Get in touch.