We align strategy to outcomes, prove value on real data, and establish the governance to run AI safely in production.
Define outcomes, value metrics, and a sequenced plan tied to budget.
Identify candidate cases, estimate lift/cost, and pick quick wins.
Map sources, quality, lineage, and privacy; close gaps before modeling.
Set policy for data, prompts, and models; define approvals and roles.
Design evaluations, run controlled pilots, and harden for production.
Clarify RACI, intake/review cadence, and change control for models.
Evaluate platforms and partners; contract for evidence and exits.
Forecast spend, measure lift, and report ROI with defensible numbers.
Teams get an executable roadmap; leaders get measurable milestones.
Align on business outcomes and constraints. Map data sources, quality, lineage, and privacy so use cases start on stable ground.
Score opportunities by expected lift, feasibility, risk, and data fitness. Pick a staged path that shows progress early.
Define evaluations and guardrails; run POCs on representative data; pilot with real users; confirm impact before scaling.
Stand up MLOps and model registries. Enforce policy for inputs/outputs, identity, keys, and environments. Document approvals and logs.
Plan change management and training. Track outcomes, costs, and risks. Iterate the roadmap as evidence accumulates.
