Enterprise AI Delivery Model
AI delivery is not a sequence of handovers. Strategy, teams, engineering, and adoption must stay connected through a continuous learning loop.
How the model works
Strategy & discovery
Name the business outcome, establish a baseline, and qualify use cases against value, feasibility, readiness, and risk.
Organize for ownership
Connect a business owner, Deployment Strategist, and embedded engineering. Give the squad access, decision rights, and enabling support.
Prototype to production
Start with a narrow task. Test it with users, integrate real systems, evaluate failure modes, and make operational ownership explicit.
Adoption in the workflow
Redesign the work, prepare users, and support exceptions. Evaluate successful task completion rather than deployment alone.
Scale with evidence
Expand when quality, economics, and ownership are proven. Return the learning to strategy and reuse capabilities across the portfolio.
Operating model across the lifecycle
Funding, governance, platform support, and ownership span every stage. They are not a final approval gate.