How I work
I own the engagement from first workshop to measured adoption.
Four phases, each with the artifact it produces and the number it moves. The same model whether the work is agentic AI or platform architecture.
- 01Identify
Frame the job to be done
Requirements workshops with the people doing the work, not only the sponsor. Every candidate is framed as a job to be done.
- What comes out
- A ranked use-case shortlist, scored on value, data readiness, and regulatory friction.
- What gets measured
- Baseline for the one metric the first use case has to move.
- 02Commit
Design it, then fund it
Solution architecture against the systems that already exist, plus a readiness assessment. The business case runs beside it. No number, no green light.
- What comes out
- Target architecture, integration map, and a business case with a named owner per workstream.
- What gets measured
- Readiness scored across data, integration, legal, and the operations team that inherits it.
- 03Deploy
Ship a thin slice with engineers
One market, one journey, real traffic. I work inside the build with the engineers, not from a steering committee.
- What comes out
- A system live in production, with the runbook and guardrails that keep it there.
- What gets measured
- The baseline metric re-measured, beside latency, containment, and error rate.
- 04Consume
Make it stick, then scale it
Adoption is a workstream, not an afterthought. Enablement for the teams who run it, then rollout market by market.
- What comes out
- Enablement material, an adoption dashboard, and a market-by-market rollout plan.
- What gets measured
- Adoption rate, hours returned to the business, and the outcome the case promised.
Have a problem worth solving at scale?
I work with teams turning AI and data strategy into systems that ship.
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