How to Establish a Pre-AI Baseline

By Paige Gilmore, Founder, NetLift · Published 2026-07-28 · Updated 2026-08-07

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.

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.

Why is a baseline essential for calculating net value?

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.

How do you source objective baseline data?

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.

What variables should be included in the baseline?

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.

How does the baseline inform decision states?

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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