Nearly every enterprise we meet has run AI pilots. Far fewer have AI running real work in production. The gap is rarely the model. It’s sequencing, ownership and the unglamorous engineering that turns a demo into a dependable system.
Days 1–15: pick the right first problem
The best first use case is valuable, measurable and contained. It touches real systems, but not every system. We score candidates on value, feasibility, data readiness and risk — and pick the one that also builds foundations for what comes next.
Days 16–45: prove it on real data
A working prototype on production-like data, evaluated against real historical cases, beats any slide. This is where we define the success metric with the business owner and agree the guardrails with risk and compliance.
Days 46–75: build for production
- Integrate with the systems of record so the AI can act, not just advise.
- Build the evaluation suite that every future change must pass.
- Design hand-offs to people with full context.
- Instrument cost, quality and latency from day one.
Days 76–90: launch, measure, hand over
Launch to a controlled share of volume, compare against the baseline, then scale. In parallel, the client team pairs with ours so that by day 90 they can run and extend the system themselves.
Speed comes from starting right, not starting first.
Ninety days is not a magic number — but a fixed horizon forces the decisions that pilots tend to postpone. If you have a pilot that’s stuck, we’d be glad to take a look.
