A pre-AI baseline is established by documenting the time and labor cost required to complete specific tasks using legacy or manual methods. This objective benchmark allows organizations to calculate realized time saved and net value once AI is deployed.
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Establishing a pre-AI baseline is the first step in moving from AI hype to financial accountability. It provides the "zero point" needed to measure how much time is actually saved and whether the labor value of that time exceeds the total cost of adoption.
Without a baseline, ROI is speculative. By capturing the time work takes without AI, finance and AI leaders can determine the true net value of their investment and assign objective decision states to every project, such as Expand or Stop.
To find the net value of an AI deployment, you must subtract the full cost—including licenses, implementation, and training—from the labor value of the time saved. The labor value is derived from the hours saved multiplied by the loaded hourly cost. Without a pre-AI baseline representing the "time the work would take without AI," there is no way to quantify the hours saved.
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Objective baselines are built from historical performance data or cohort data. Historical data looks at past performance on the same tasks, while cohort data compares a non-AI group against an AI-enabled group. In the NetLift methodology, these objective sources rank higher in Evidence Quality than self-reported estimates. High-quality evidence ensures that the final payback calculation is credible to the CFO.
The baseline must focus on the work itself rather than individual productivity. Key metrics include the average time per unit of work and the volume of work produced. When calculating costs, NetLift uses a default loaded staff cost of $75 per hour and assumes 4.33 working weeks per month. These figures can be adjusted to match specific departmental realities, but they provide the necessary framework for a deterministic value model.
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Once a baseline is set and AI work is tracked, the performance is categorized into one of five decision states: Expand, Continue, Review, Improve, or Stop. If the time saved against the baseline does not cover the cost of licenses and rework, the project may move to "Review" or "Stop." Conversely, high net value relative to the baseline justifies an "Expand" decision. This approach treats AI adoption as a portfolio of measurable work units.
NetLift establishes value by comparing the tracked time of AI-assisted work against your pre-AI baseline. This deterministic model calculates the current net value—subtracting license, usage, and training costs—while grading the Evidence Quality from Estimate Only to Verified. This ensures that finance teams see a clear, surveillance-free path to payback based on actual time saved.
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About the author
Paige Gilmore · Founder, NetLift
Paige Gilmore is the founder of NetLift, the AI Value Management platform that helps organisations measure the cost, savings and return of AI adoption.
Paige Gilmore on LinkedInNetLift calculates AI value by subtracting the time taken to complete work with AI from a verified baseline, then deducting the total cost of ownership—including licenses, training, and rework—to determine net return.
Read ai-roiAvoid double-counting AI value by strictly separating realised savings from future projections and subtracting the full cost of adoption, including rework and training, from gross labour value.
Read ai-roiAI quality is measured by the reduction in human review and rework time required to achieve a finished output. Higher quality models decrease the time spent on corrections, directly increasing the net value and shortening the payback period.
Read ai-roiAI rework is measured by tracking the human time spent reviewing and correcting AI-generated output, then multiplying those hours by the loaded staff cost. This total is subtracted from the gross labour value of time saved to determine the net value of an AI implementation.
Read ai-roiAI payback is the time required for the net labor value of realized time savings to cover the total investment cost, including licenses, implementation, and training. It is calculated by dividing the total cost to date by the monthly realized net value.
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