A finance-credible AI business case template focuses on the net value of time saved compared to the total cost of ownership. To calculate this, subtract the full AI cost (including licenses, usage, implementation, and training) from the labor value of the hours saved. Using a default loaded staff cost of $75 per hour, organizations can determine exactly how long it takes for the investment to pay back.
Effective templates distinguish between realized value and future projections. Realized value is based on work already completed, while future value estimates recurring savings based on expected volume. Every measured area should result in a clear decision state: Expand, Continue, Review, Improve, or Stop.
How do you determine the labor value of AI?
Labor value is calculated by multiplying the hours saved by the loaded hourly cost of the staff involved. Hours saved is defined as the time the work would have taken without AI minus the time it took with AI. For a business case to be accurate, it must also track the time spent on review and rework to ensure the net time savings are realistic.
What is Evidence Quality in an AI business case?
Evidence Quality grades the strength of the data used to justify an AI spend. This scale ranges from 'Estimate Only' to 'Verified.' Finance-credible cases prioritize objective baselines, such as historical data or cohort data, over individual self-estimates. Factors including sample size, recency, and cost completeness determine how much confidence a buyer should have in the projected return.
NetLift automates this measurement by comparing tracked work against deterministic baselines. Rather than relying on individual productivity metrics or intrusive surveillance like keystroke logging, NetLift focuses on the net value of the work itself. This allows teams to see the exact payback period and move from estimates to verified value.