Pilot and Rollout Planning
In brief: Pilot domain selection is frequently influenced by organizational politics rather than objective criteria — the domain whose business owner is most enthusiastic, or the domain the implementation team finds most technically interesting, ends up as the pilot rather than the one that would produce the most compelling evidence for Phase 2 investment.
Module support notes
Pilot Domain Selection in Practice
Pilot domain selection is frequently influenced by organizational politics rather than objective criteria — the domain whose business owner is most enthusiastic, or the domain the implementation team finds most technically interesting, ends up as the pilot rather than the one that would produce the most compelling evidence for Phase 2 investment.
A scoring model that evaluates candidates against consistent, documented criteria produces a selection that is both more likely to succeed and more defensible to stakeholders who expected their domain to be chosen first.
Tip
Run a documented scoring exercise before finalizing the pilot domain — even if the answer seems obvious. Score each candidate across business pain intensity, AI dependency urgency, data complexity, and stakeholder readiness. Documenting the scores and selection rationale gives you a defensible answer for every domain owner who asks why theirs wasn't chosen first, and produces a reusable sequencing framework for every domain decision that follows.
The Pilot as an AI Architecture Test
The pilot is the ideal — and often the only — controlled opportunity to validate the MDM-to-AI architecture end-to-end before it is scaled across multiple domains and AI use cases. Architecture gaps discovered in the pilot can be addressed in a controlled environment with limited blast radius.
The same gaps discovered mid-rollout, when multiple AI teams are depending on the pipeline, require program-wide fixes that disrupt active AI development. Common gaps surfaced during pilots include a feature store that doesn't receive provenance metadata, a certification workflow that produces records in the wrong format for the AI model registry, and a drift monitoring connection that doesn't align with the MDM platform's quality event schema.
Warning
Do not skip end-to-end AI pipeline testing in the pilot on the assumption that it can be validated during rollout. Architecture gaps found during a pilot affect one domain and one AI use case — they can be fixed in weeks. The same gaps found mid-rollout affect every domain and AI team currently depending on the pipeline, and fixing them requires a program-wide pause. The pilot is the only moment when the cost of finding a gap is low.
The pilot proves what works. The rollout scales what the pilot proved — not what the architecture assumed.
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