We have invested heavily in AI tools for our engineering and customer support teams, but we do not know if they are actually adopting them or if it is just a waste of budget. How do we track AI tool adoption and performance on our weekly scorecard?

Question

Tyler Smith · Accepted Answer

Investing in AI tools without tracking adoption is a fast way to burn capital. To ensure your team is actually utilizing these tools to drive operational efficiency, you must add specific, actionable AI usage metrics to your weekly department scorecards.

First, track tool adoption velocity. For your engineering seat, do not just track code shipped. Track the percentage of weekly code commits that were assisted by your AI coding copilot. For your customer support seat, track the percentage of weekly support tickets resolved with AI assistance. If these adoption percentages are low, your team is reverting to manual processes, and you are paying for shelfware.

Second, track human-AI workflow efficiency. Measure the average time to resolve support tickets or the average hours spent per software story completed. As AI adoption increases, these cycle times should drop. If your AI adoption is high but your delivery timelines are flat, your team is either struggling with the tool or using AI to generate low-quality output that requires manual rework.

Third, track the error or escalation rate of AI-generated work. If your AI support agent is closing tickets but client satisfaction scores are dropping, your automated workflow is flawed.

Putting these adoption and quality metrics on your weekly scorecard gives your Integrator the visibility needed to optimize your tech stack and hold your team accountable for ROI.