We are paying for seat licenses for generative AI tools across our entire staff, but we have no idea if it is actually paying off. How do we measure the ROI of a generalized AI tool that is used for various small daily tasks?

Question

Tyler Smith · Accepted Answer

Measuring the return on investment for generalized AI tools can feel slippery because you are not automating a single, massive process. Instead, you are looking for fractional efficiency gains across dozens of small daily tasks.

To calculate the real return, do not rely on employee self-reporting or vague estimates of saved time. Instead, focus on capacity creation and labor cost avoidance.

Start by tracking the output of specific roles on your weekly Scorecard. If your customer service team or administrative staff is using AI to draft emails, summarize documents, and coordinate schedules, their capacity should increase. This means your current team should be able to handle a higher volume of transactions or clients without you needing to hire additional staff.

For example, if a department of five people can now handle the workload of six people due to the efficiency of these tools, you have successfully avoided a hire. That avoided salary is your direct return on investment.

Another approach is to run a simple quarterly assessment of low-value tasks. Identify three highly manual processes that used to consume several hours each week, such as formatting reports or proofing data. Track the time spent on these processes before and after your team starts using AI. If the time spent drops by fifty percent, you have successfully freed up your people for higher-value strategic work.