Departmental AI reports must provide a deterministic view of value to satisfy CFO and Board requirements. By calculating the labour value of time saved against a loaded hourly cost and subtracting the total cost of ownership, organisations can identify exactly which AI initiatives are paying back and which require intervention.
NetLift delivers this clarity by measuring work outcomes rather than monitoring individual employees. This methodology ensures that every measured area is assigned a clear decision state—such as Expand, Review, or Stop—based on the net return and the strength of the evidence behind the numbers.
How is departmental net value calculated?
Net value is the labour value of realised time saved minus the full cost of the AI initiative. To find the labour value, we multiply the hours saved by the loaded hourly cost (defaulting to $75). The full cost must include not just licences and usage, but also implementation, training, and the cost of human review or rework where tracked. Future value, or expected recurring savings, is always reported separately from realised net value to maintain financial integrity.
What are the five decision states for AI spend?
Every department or project in the report is assigned one of five states: Expand, Continue, Review, Improve, or Stop. These states are determined by the net value and the payback period—the time it takes for net value to cover the total AI investment to date. This framework allows the Head of AI or CIO to stop underperforming projects and reallocate budget to initiatives that demonstrate a clear, verified return.
How does Evidence Quality impact the ROI report?
Evidence Quality grades the reliability of the data, ranging from "Estimate Only" to "Verified." Numbers based on objective baselines, such as historical data or cohort comparisons, carry more weight than self-estimated savings. The report considers sample size, recency of data, and cost completeness to ensure that the board can distinguish between speculative projections and hard financial facts.
Why is this approach different from employee surveillance?
Effective AI value management measures work, not people. The reporting methodology explicitly avoids invasive surveillance techniques like keystroke logging, screenshots, or browser monitoring. Instead, it focuses on the time work would take without AI compared to the time it takes with AI. This protects employee trust while providing the CIO and CFO with the deterministic productivity data they need to justify spend.
To determine if your AI investment is truly paying back, you must move beyond usage stats to net value. NetLift measures time saved against a deterministic baseline and subtracts the full cost of implementation and rework. By grading this data with Evidence Quality, it provides a clear signal on whether to expand or stop departmental AI spend.