Context
A leadership team with a successful pilot, a backlog of AI ideas, and no shared picture of how any of it should fit the company’s systems, data, and accountability structure.
The problem
The pilot worked in isolation but touched none of the systems, permissions, or data governance that production would require.
Every function had candidate use cases; nobody had comparable economics for them.
Vendor claims, build options, and platform bets were being evaluated ad hoc.
The system
Critical decisions
Workflow decomposition before model selection
Mapping each workflow into judged, computed, and generated steps shows where AI belongs — and where it does not.
A reference architecture the whole portfolio shares
Identity, data access, evaluation, and human-oversight patterns defined once, so each use case is a configuration, not a new platform decision.
Sequence by value against integration cost
The roadmap orders work by measurable business impact per unit of integration effort — not by demo appeal.
Execution
Executive interviews, workflow mapping, data and systems review, economic scoring, build-vs-buy analysis, and a prioritized 6–12 month roadmap with the first production candidate specified to implementation depth.
Outcome
- A prioritized AI roadmap tied to business outcomes — not a catalog of experiments.
- A reference architecture that later use cases inherit.
- [Quantified outcomes to confirm before publication]
Lessons
- Pilots prove capability; architecture decides value.
- The scarce resource is integration capacity, not ideas.
- Build-vs-buy is a portfolio decision, not a per-project coin flip.
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