An Enterprise AI Transformation Roadmap That Survives Contact With Reality

Phase 1 — Pick problems, not technologies

Start from a business process that already has a measurable cost: reconciliation effort, claim turnaround, exception handling, report preparation. If you cannot describe today's cost in hours, currency, or error rate, you will not be able to prove the value of the AI that replaces it.

• Write the outcome as a number you already track today.
• Confirm a named business owner who will accept or reject the result.
• Check that the process runs often enough for automation to compound.

Phase 2 — Make the data usable before the model

Data readiness is where timelines actually slip. Before any modelling work, map the source systems, the refresh cadence, the fields that carry business meaning, and the points where records are reconciled. Lineage and access control belong in this phase, not after the pilot succeeds.

Phase 3 — Build a thin production slice

A pilot that runs on an extract in a notebook proves very little. Build the narrowest possible end-to-end path: real source data, real inference, real output landing where the business user already works. A thin slice in production teaches you more in three weeks than a broad prototype does in three months.

Phase 4 — Design the human checkpoint

In regulated and finance-heavy workflows, an AI output needs a reviewer, an audit trail, and an override path. Decide early what a reviewer sees, what they can change, and how their corrections flow back into evaluation. This is what turns a demo into a system a risk function will approve.

Phase 5 — Operate, measure, and expand

Once live, track model behaviour and business outcome separately. Drift, latency, and cost per run are engineering metrics; hours saved and exception rate are business metrics. Expansion should be justified by the second set, not the first.