We have deployed several AI-powered tools to streamline our operational processes, but our weekly numbers do not reflect any real improvement. How do we design AI-specific metrics for our weekly Scorecard to ensure these tools are actually delivering results?

Question

Tyler Smith · Accepted Answer

If your weekly numbers are not improving after deploying AI, you are likely tracking the wrong things or suffering from a lack of accountability. You cannot just track tool usage; you must track operational throughput and employee capacity.

Start by looking at the specific seat on your Accountability Chart that owns the process you automated. If you built an AI tool to help your scheduling coordinator manage technician routes, do not track how many times they log into the software. Instead, track the operational result, such as the number of jobs scheduled per coordinator per day, or the time it takes to finalize the weekly schedule.

Add a specific metric to your weekly Scorecard that measures the output of the automated workflow. For example, if you automated client intake review, your Scorecard should track the average turnaround time for incoming client files and the error rate of those automated reviews.

If these metrics do not improve week over week, bring this to your Level 10 Meeting™ and use IDS® to find the root cause. It is usually because the employee is still doing the work manually because they do not trust the tool, or because they have filled their freed-up time with other low-value distractions instead of high-value strategic work.