How I work
I own the deployment from first workshop to measured adoption.
Four phases, each with the artifact it produces and the number it moves.
- 01 · Identify
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.
- Artifact
- A ranked use-case shortlist, scored on value, data readiness, and regulatory friction.
- Number
- Baseline for the one metric the first use case has to move.
- 02 · Commit
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.
- Artifact
- Target architecture, integration map, and a business case with a named owner per workstream.
- Number
- Readiness scored across data, integration, legal, and the operations team that inherits it.
- 03 · Deploy
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.
- Artifact
- A system live in production, with the runbook and guardrails that keep it there.
- Number
- The baseline metric re-measured, beside latency, containment, and error rate.
- 04 · Consume
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.
- Artifact
- Enablement material, an adoption dashboard, and a market-by-market rollout plan.
- Number
- Adoption rate, hours returned to the business, and the outcome the case promised.
The same model whether it's an agentic AI use case or a platform migration.
Building an AI platform, or getting agentic AI adopted at scale?
I work with teams turning AI and data strategy into systems that ship — and stick.
Start a conversation