An AI licence renewal scorecard provides a structured framework to evaluate if software spend translates into measurable business value. By scoring key indicators of adoption and ROI, finance and procurement teams can decide whether to expand seat counts or stop a service before the next billing cycle.
Decisions are categorised into five states: Expand, Continue, Review, Improve, or Stop. This approach moves beyond user sentiment to focus on deterministic metrics, such as the labour value of time saved compared to the full cost of implementation and training.
How this calculator works
Each yes answer scores one point. 7+ = Expand, 5-6 = Continue, 3-4 = Review, under 3 = Improve or Stop before renewal.
Every result is computed in your browser from the numbers you enter — nothing is estimated for you. The same formulas run inside NetLift on verified tracked work, where results carry an Evidence Quality grade.
How do you score an AI renewal?
The scorecard uses a 10-point system where each positive indicator earns one point. A score of 7 or higher indicates the tool is delivering significant value and is ready for expansion. Scores between 5 and 6 suggest you should continue current usage, while 3 to 4 points trigger a formal review. Any score under 3 suggests the implementation is failing to meet objectives and should be improved or stopped.
What metrics drive the renewal decision?
Renewal decisions should rely on net value, calculated as the labour value of realised time saved minus the full AI cost. This cost includes the licence fee, usage, implementation, training, and any tracked review or rework. Calculating the payback period—how long it takes for this net value to cover the total investment—ensures the renewal is fiscally sound.
NetLift measures AI value by comparing the time work takes with AI against objective historical baselines. This methodology focuses on work outcomes and net return rather than individual productivity or surveillance. By applying Evidence Quality grades to every calculation, NetLift ensures that renewal decisions are backed by verified data rather than self-estimates, identifying exactly which tools have reached a positive payback state.