An AI vendor evaluation scorecard must move beyond feature comparisons to focus on financial utility. To determine if an AI tool is worth the investment, procurement teams must measure the labor value of time saved against the total cost of ownership, including implementation and review time.
For finance and technology leaders, the goal is to identify a clear payback period. By measuring work rather than individual productivity, organizations can verify if a tool delivers enough value to reach an 'Expand' or 'Continue' decision state without relying on vendor hype.
What financial metrics should be on an AI scorecard?
A finance-credible scorecard focuses on Current Net Value. This is calculated by taking the labor value of realized time saved and subtracting the full AI cost. The full cost must include licenses, usage, implementation, training, and the cost of human review or rework.
Time saved is the difference between how long a task would take without AI and how long it takes with the tool. To convert this into a dollar amount, multiply the hours saved by the loaded hourly cost of the staff involved. A standard default assumption for this is $75 per hour.
How do you grade the quality of vendor evidence?
Not all performance data is equal. A robust scorecard grades evidence quality from 'Estimate Only' up to 'Verified.' Objective baselines, such as historical data or cohort comparisons, are ranked higher than subjective self-estimates.
Factors such as sample size, the recency of the data, and the completeness of the cost reporting determine the strength of the evidence. This prevents procurement from making decisions based on optimistic projections that lack a deterministic foundation.
What are the five decision states for AI adoption?
Every measured area in an AI evaluation should result in one of five clear outcomes. 'Expand' and 'Continue' are for tools showing positive net value and strong evidence. 'Review' and 'Improve' are for tools that show potential but require optimization in usage or implementation costs.
'Stop' is reserved for tools where the net value does not justify the cost or where the payback period is too long. Stating future value separately from realized value ensures that decisions are based on what has actually been achieved, not just what is expected in the future.
How do you measure value without employee surveillance?
Measuring AI value does not require monitoring individual employees. A credible evaluation focuses on work and value output rather than keystrokes or browser activity. This methodology avoids surveillance tactics such as screenshots, keystroke logging, or browser monitoring.
By focusing on the work performed, the organization can track the time saved on specific tasks. This maintains trust while providing the CFO with the necessary data to verify the return on AI spend.
NetLift provides a deterministic framework for these evaluations, measuring time saved against objective baselines to calculate net value. By grading evidence quality and tracking full costs—including review and rework—NetLift allows leaders to see exactly when an AI investment reaches payback.