To calculate future AI value, multiply the expected recurring time savings by the anticipated volume of those tasks. This provides a gross labour value that must be offset by the full cost of the AI, including licenses, usage fees, and the time required for human review and rework.
Projecting value requires a shift from vague productivity claims to deterministic models. By using a loaded hourly cost—defaulting to $75 per hour in standard benchmarks—finance leaders can convert time saved into a hard currency value that informs the business case for expansion.
How is the core value of AI time savings measured?
The foundation of future value is the time saved per task. This is calculated by subtracting the time taken to complete a task with AI from the time it would have taken without it. To find the labour value, multiply these hours saved by the loaded hourly cost of the staff performing the work. This deterministic approach ensures that value is tied to specific work outputs rather than abstract sentiment.
Why must future and realised value be separated?
Realised value accounts for time savings that have already occurred and been tracked. Future value represents the expected recurring savings based on current performance and projected volume. Keeping these figures separate prevents the inflation of current financial reports with speculative gains. It allows a CIO or CFO to see exactly what has been earned to date versus what the investment is expected to yield over the coming months.
What costs must be deducted from the gross value?
Calculating net value requires subtracting the total cost of ownership from the gross labour value. This includes obvious expenses like seat licenses and usage fees, but also implementation, training, and human review. If an AI output requires significant rework, that time must be tracked and subtracted from the total savings. Only after these deductions can you determine the true payback period.
How does evidence quality affect financial projections?
Not all value claims are equal. Projections should be graded based on the strength of the underlying data, ranging from 'Estimate Only' to 'Verified.' Objective baselines, such as historical data or cohort comparisons, provide higher evidence quality than self-reported estimates. High-quality evidence reduces the risk of the 'gutted value' scenario, where changes in subscription terms or AI performance overnight could otherwise invalidate the entire business case.
NetLift provides a deterministic framework to measure whether AI spend actually pays back by comparing tracked work against objective baselines. By assigning an Evidence Quality grade to every calculation, NetLift ensures that future value projections are grounded in reality, helping leaders decide whether to Expand, Continue, Review, Improve, or Stop a specific deployment based on actual net return.