Successful AI procurement focuses on net value: the labour value of verified time savings minus the total cost of ownership, including implementation and rework. It requires a move away from license-counting toward evidence-based decision states like Expand, Review, or Stop.
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AI procurement requires a shift from tracking seat counts to tracking net value. CFOs and CIOs must verify that the labour value of time saved actually exceeds the total cost of licenses, implementation, and human review.
This checklist provides a framework for measuring AI adoption through deterministic value models. It ensures that every investment is backed by evidence quality rather than optimistic estimates or vendor hype.
The net value of AI is the labour value of realised time saved minus the full cost of adoption. To find the labour value, multiply the hours saved by the loaded hourly cost. A common default for this is $75 per hour.
The total cost must include more than just the license fee. Procurement teams must account for usage fees, implementation, initial training, and the time humans spend reviewing or reworking AI output where that is tracked.
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Every AI initiative should be assigned one of five decision states based on its performance: Expand, Continue, Review, Improve, or Stop. These states are determined by comparing the net value against the initial investment and the evidence quality of the savings.
A project marked "Expand" has verified time savings and a clear payback period. A project marked "Review" or "Stop" lacks sufficient net value or relies on weak evidence that does not justify the current spend.
Not all efficiency claims are equal. Procurement should grade evidence from "Estimate Only" up to "Verified." Objective baselines, such as historical data or cohort comparisons, provide the strongest evidence for a business case.
Self-estimates are the lowest form of evidence. High-quality procurement processes require larger sample sizes, recent data, and a complete picture of costs to move an AI tool from a pilot phase to a full rollout.
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Realised value reflects work that has already been completed and time that has already been saved. Future value is a projection of expected recurring savings based on expected volume over time.
Stating these values separately prevents "phantom ROI." CFOs need to see what has actually been saved to date to calculate the true payback period of the AI investment before committing to further recurring costs.
Ethical AI procurement avoids individual productivity tracking. The focus should be on the work and the value created by the technology, not the person using it.
This means no keystroke logging, no screenshots, and no browser monitoring. Measuring the time a task takes with AI versus a baseline is a financial audit of a process, not surveillance of an employee.
NetLift provides a deterministic model to track whether AI spend actually pays back. It compares time saved against a baseline to calculate a current net value, factoring in a $75 default loaded staff cost and all implementation expenses. By grading evidence quality, NetLift gives procurement a clear decision state—such as Expand or Stop—for every AI tool in the stack.
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About the author
Paige Gilmore · Founder, NetLift
Paige Gilmore is the founder of NetLift, the AI Value Management platform that helps organisations measure the cost, savings and return of AI adoption.
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