The AI project has been delivered. The integration works, the team has received its training and the project status is green. Yet in the next management meeting, I would still ask one question: who can confirm that operations have actually improved as a result?
A successful implementation deserves recognition. But a working application is not yet evidence of shorter delivery times, less rework or a better margin. If the project manager moves on after completion and the executive team sees only the technical status, the question can easily be left unanswered. The organisation pays for the system regardless.
37% of respondents report a positive AI contribution to the earnings before interest and tax (EBIT) of their organisation as a whole. This is the finding from McKinsey’s 2026 global survey. It covers 1,719 participants in 97 countries and is based on self-reports, not audited project returns. The remaining responses are therefore not evidence of failed AI projects: individual applications may deliver value before that value is visible in the organisation-wide result. 1
For the executive team, this does not create a new target percentage. It creates a practical task: test the expected benefit where the work is done, rather than confusing it with completion status.
What remains open after technical sign-off
IT can confirm whether integration, access and technical operational readiness work within its area of responsibility. The business leader accountable for the process must additionally judge whether the application improves the agreed way of working. Finance can test the economic case. None of these perspectives replaces the others.
I would explicitly distinguish between technical sign-off, transition into operations and the later benefit review. This is not a call for three new governance forums. It means not treating the first signature as an answer to every question that follows. Accountability for benefits and measurement after handover are also embedded in the Project Management Institute’s approach to benefits management. 2
A project may be administratively complete while its benefits are still being tested. In that case, the open assumption, the named owner and the next review date must remain visible. Otherwise, an economic promise disappears with the project list.
Acceptance belongs in the real workflow
Consider an explicitly illustrative situation: an AI assistant produces draft proposals. In testing, it produces them more quickly. In day-to-day work, however, sales staff must check contractual clauses, find missing information and correct unusual client requirements. The system may still be worthwhile. But the speed of the first draft is the wrong measure if used on its own.
I would examine the time from a complete client brief to a proposal approved by the relevant business function. That includes the total handling time of everyone involved, including review and correction. Waiting time is recorded separately. A faster draft that waits longer for approval does not automatically speed up the response to the client.
A baseline requires comparable proposals completed without the new method. Complexity, case selection and quality requirements must be traceable. Comparing straightforward AI cases with previously difficult cases would be convenient, but not robust. If the baseline is missing, the benefit remains an assumption to be tested.
The assessment must also include the cost of operating the AI system and the quality of the result. Time released is initially additional capacity. A genuine cost saving arises only when expenditure can demonstrably be reduced. If the same time is used instead to produce further proposals, that benefit requires different evidence.
Nor does the review end with the first successful run. Data sources, models and user behaviour can change. NIST’s AI Risk Management Framework calls for monitoring function and behaviour in production. This supports operational control, but it is not evidence of an economic return. 3
What I would change in the next review
I would select an AI application that is already in use and ask the business owner to set out the expected benefit on one page. Which outcome should improve? How was the work done before? What does current operation show? And what additional work has the system created?
For the proposal process described, an initial benefit review after 30 days could be sensible, provided that enough comparable cases are available by then. This is a working arrangement, not a universal deadline. Infrequent or highly variable processes require a different observation period.
The decision may then differ: continue, improve in a focused way or limit use. Sometimes the system is sound and the handover is weak. Sometimes the additional control effort is greater than the capacity gained. Both should be known before the application is rolled out to further teams.
This benefits review is one building block of a manageable AI Operating System. Business accountability remains in operations, technical support is clear and the executive team receives a testable benefit rather than another green status indicator. If implementation after a sound transformation decision is already under control, additional support is not needed; where there is a genuine delivery gap, AI Transformation Execution Partnership helps with workstream leadership, value evidence and internal knowledge transfer.
Sources
McKinsey, global AI survey 2026, conducted from 4 May to 8 June; economic impact reported by respondents. 1
Project Management Institute, professional article on planning, handover and ongoing tracking of project benefits. 2
NIST, AI Risk Management Framework 1.0, 2023, particularly MEASURE 2.4 on monitoring in operation. 3