Phase the work so value shows at every milestone
Finance will judge an AI control plane program against published payback data, and that data sets a long horizon. In Deloitte’s 2025 survey of 1,854 executives in Europe and the Middle East, most achieved satisfactory ROI on a typical AI use case within two to four years, against the seven to 12 months expected of technology investments.1 A program that phases its work can show value well inside that horizon, one milestone at a time.
Each milestone has value and evidence
The handbook’s plan runs for 24 months in four phases. Each phase ends in a milestone the business can see, with evidence to report at every steering review.
| Milestone | Value delivered | Evidence to report |
|---|---|---|
| Month 3, one front door | Every known agent registered with an owner and a tier Model and tool spend visible by use case Duplicate and unused agents retired The first use-case slice live in assist mode |
Agent coverage Governed traffic Baseline cost per outcome The first slice’s KPIs against baseline |
| Month 8, reusable capabilities | New agents reuse governed APIs and MCP tools in place of new integrations Routine tasks move to lower-cost models within token limits Use cases scale beyond the pilot |
Reuse of published capabilities Cost per outcome trend Run-rate value of live slices against plan |
| Month 14, trusted autonomy | Agents act in systems of record under approvals bound to each action, which is where most of each business case sits Use-case KPI targets met by month 12 Incidents contained in minutes |
Oversight coverage and effectiveness Time to contain Evaluation pass rate Use-case KPIs at target |
| Month 24, north star reached | All goals met Use cases at full run rate and sustained New use cases start on shared controls at lower cost and risk Models swappable without application changes Post-quantum migration on track |
The full scorecard at target Cumulative value against each business case |
The architecture delivers value of its own before any use case reaches full scale, from spend visibility at month 3 to faster delivery of every new agent at month 8. Each milestone also brings more of a real use case into production, starting with a first slice that works in assist mode and moving to agents that act in systems of record once the controls are in place.
Expectations of AI payback are shortening. In KPMG’s 2025 Global CEO Outlook, 67% of 1,350 CEOs expected returns on AI within one to three years, where a year earlier most expected three to five.2 Google Cloud’s 2026 survey of 2,403 executives found 84% seeing increasing financial returns from AI and 26% seeing returns accelerate year over year.3 Phasing lets a program beat the two-to-four-year benchmark, because read-only and propose-only slices release capacity within months and help fund the controls that the larger gains depend on.
Deloitte’s survey also found that 85% of the leaders it studied use different frameworks or timeframes for generative and agentic AI.1 A program can do the same, judging its early slices over months and the agents that act in systems of record over a longer horizon.
What to do next
- Agree the four milestones and the evidence for each with finance before the first phase starts, so every steering review reports against them.
- Pick a first use-case slice that can run in assist mode by month 3, so the program shows value while the larger controls are still being built.
- Set one horizon for read-only and propose-only slices and a longer one for agents that act in systems of record, and report each against its own.
Go deeper
Section 8 of The AI Control Plane Handbook sets out the four phases, how three reference use cases phase in, and the first 90 days. Slides 26 and 27 of the executive deck show the plan and the value at each milestone.
Found this useful? Follow me on LinkedIn for the next one.
Working on this? Connect and tell me what you are seeing.
Read the full handbook and the executive deck online.