Evidence is sufficient when it provides a deterministic view of net value that accounts for the full cost of adoption. For finance and technology leaders, 'enough' evidence means moving beyond anecdotal time savings to a calculation that subtracts licenses, implementation, training, and rework from the labor value of time saved.
NetLift uses an Evidence Quality scale that grades data from 'Estimate Only' to 'Verified.' High-quality evidence relies on objective baselines, such as historical data or cohort comparisons, rather than self-reported estimates. This ensures that decisions to scale AI are based on realized net value rather than speculative productivity gains.
What defines high-quality evidence in AI measurement?
High-quality evidence is defined by its objectivity and completeness. To move a metric from an estimate to a verified state, the measurement must use objective baselines—comparing work completed with AI against historical data or a control cohort. Evidence quality is also determined by sample size, the recency of the data, and how thoroughly costs have been captured. Relying on self-estimates often leads to inflated ROI figures that do not hold up under financial scrutiny.
How should net value be calculated for CFO approval?
Proving ROI requires a full accounting of costs, not just license fees. Net value is found by taking the labor value of realized time saved and subtracting the full cost of AI. This cost includes licenses, usage fees, implementation, training, and any time spent on review and rework. By including these factors, the payback period—the time it takes for net value to cover the total cost to date—becomes a credible metric for procurement and risk teams.
Why must realized and future value be separated?
To maintain financial integrity, realized value and future value must always be stated separately. Realized value represents the net gains already achieved through tracked work. Future value is a projection based on expected recurring time savings and anticipated volume. Mixing these figures creates a risk of reporting 'soft' savings that have not yet materialized, which can lead to poor capital allocation decisions.
How do decision states guide AI adoption?
Sufficient evidence allows every measured AI area to be assigned one of five decision states: Expand, Continue, Review, Improve, or Stop. An 'Expand' decision is only appropriate when evidence quality is high and net value is positive. If evidence remains at the 'Estimate Only' stage, or if the net value does not cover the cost of implementation and training, the initiative should remain in 'Review' or 'Improve' until the data justifies further investment.
NetLift measures the time saved on tracked work against objective baselines to determine the labor value of that time. By grading the strength of this data from Estimate Only to Verified, NetLift provides a clear path to proving whether an AI spend pays back. This measurement focuses on work and value rather than individual productivity, ensuring ROI is proven without the need for employee surveillance like screenshots or keystroke logging.