A finance-credible AI renewal business case focuses on net value: the labour value of realised time saved minus the full cost of the software, implementation, and human oversight. Instead of relying on qualitative sentiment, this methodology uses objective baselines to prove how much time AI has actually removed from specific work streams.
By calculating the payback period and assigning an Evidence Quality grade to each data point, organizations can identify which tools are delivering a return and which are creating hidden costs through rework or low adoption. This ensures the renewal decision is based on verified net return rather than speculative future value.
How do you calculate the net value of an AI renewal?
Net value is determined by taking the labour value of realised time savings and subtracting the total cost of ownership. Labour value is calculated by multiplying the hours saved on specific work by the loaded hourly cost of the staff performing it (using a default of $75 per hour in the NetLift model).
To be credible for a CFO, the cost side must include more than just subscription fees. It must factor in usage costs, implementation time, initial training, and the time spent on human review and rework where those elements are tracked. A positive net value indicates the AI has already paid for itself through productivity gains.
What is the difference between realised and future value?
Realised value is the historical proof of performance. it is the labour value of time already saved minus all costs incurred to date. This figure is used to calculate the payback period—the exact amount of time it took for the net value to cover the initial and ongoing AI investment.
Future value is an estimate of expected recurring time savings multiplied by expected volume. In a robust business case, future value is always stated separately from realised value. This prevents unproven projections from obscuring the actual performance of the AI stack during the previous contract term.
How is evidence quality graded for renewals?
Evidence Quality grades indicate how much trust the CIO and Finance team can place in the ROI figures. These grades range from "Estimate Only" up to "Verified." Numbers derived from objective baselines, such as historical data or cohort comparisons, rank higher than self-estimated productivity gains.
Factors such as sample size, the recency of the data, and the completeness of cost tracking determine the grade. A business case with high net value but low Evidence Quality suggests a need for better measurement before a full renewal is approved.
What are the five decision states for AI spend?
Every AI implementation in the business case receives one of five statuses: Expand, Continue, Review, Improve, or Stop. Tools that show high net value and strong Evidence Quality are candidates for expansion.
Tools marked as "Review" or "Improve" often have high costs related to rework or implementation that are currently negating the time savings. If a tool fails to move toward a positive net value or lacks sufficient evidence of impact, it is categorized as "Stop," allowing the budget to be reallocated to higher-performing AI initiatives.
NetLift provides the deterministic data needed for renewals by measuring the time saved on tracked work versus objective baselines. It quantifies the net return and assigns an Evidence Quality grade to every claim, ensuring the business case is built on verified performance. Importantly, this is achieved without employee surveillance; NetLift does not use screenshots, keystroke logging, or browser monitoring.