An AI pilot scorecard is a financial framework used to determine if an AI initiative delivers a net return. It moves beyond speculative hype by tracking the time saved against the total cost of adoption, providing leadership with a clear view of realised value versus projected gains.
Instead of monitoring individual activity, the scorecard evaluates work-based outcomes. Every measured area is assigned an Evidence Quality grade and a decision state, ensuring that further investment is based on verified financial performance rather than sentiment.
How do you calculate the net value of an AI pilot?
Net value is determined by subtracting the full cost of AI from the labour value of realised time saved. The labour value is calculated by multiplying the hours saved—defined as the time the work would take without AI minus the time with AI—by the loaded hourly cost. In NetLift models, the default assumption for loaded staff cost is $75 per hour unless specified otherwise.
To ensure financial accuracy, the cost side of the equation must include more than just software licences. It factors in usage fees, implementation, initial training, and the cost of human review or rework where tracked. This creates a realistic picture of the investment's performance.
What are the five decision states for AI adoption?
A scorecard provides a clear path forward by assigning one of five states to every measured area. "Expand" is reserved for high-value initiatives with strong evidence, while "Continue" applies to those meeting expectations. If an initiative shows potential but lacks performance, it is marked as "Improve." Areas with low value or poor data are marked for "Review," and those with no clear path to net value are designated as "Stop."
How is evidence quality graded?
Financial credibility depends on the strength of the data. Evidence Quality is graded on a scale from Estimate Only up to Verified. Objective baselines, such as historical data or cohort comparisons, rank higher than self-estimates. The grade also considers the sample size, the recency of the data, and how completely the costs have been captured.
Why separate realised value from future value?
Realised value represents the time and money already saved. Future value is a projection based on expected recurring savings and anticipated work volumes. Stating these separately prevents the confusion of theoretical gains with actual budget impact. This distinction allows the CFO to see exactly when the project reaches payback—the point where net value covers the total cost incurred to date.
NetLift measures the time saved on specific work tasks compared to a baseline to calculate a deterministic net return. By factoring in the full cost of licenses, training, and rework, it determines the exact payback period while grading the evidence quality of every claim. This approach ensures AI adoption is managed as a financial asset rather than an unmonitored expense, without resorting to employee surveillance.