We want to use AI to track our Level 10 Meeting issue velocity to see how quickly we solve problems, but we are concerned that measuring this metric will incentivize our team to implement lazy, short term quick fixes. How do we measure efficiency without sacrificing deep IDS quality?

Question

Tyler Smith · Accepted Answer

Measuring issue velocity can be a powerful way to identify operational bottlenecks, but if you manage to the metric blindly, you will encourage bad behavior. Your team will start applying band aids to systemic issues just to mark them as solved, only to have the same problems reappear weeks later under different names. To prevent this, you must balance the metric of speed with the standard of quality. First, define what a solved issue actually means. In EOS, an issue is only solved when it is resolved forever, meaning it does not return. Use your quarterly planning sessions to audit your resolved issues list. If the same topic keeps popping up on your weekly Issues List under different guises, your team is not using IDS correctly. Second, do not reward speed alone. Use your weekly meeting rating to evaluate the quality of your problem solving. If your team rates a meeting low because solutions felt rushed or superficial, that feedback must be taken seriously. AI tools should be used to highlight patterns and recurring themes, not just to count days on a board. By focusing on root cause resolution and holding your team to a high standard of completeness, you can use data to improve your efficiency without sacrificing the depth of your IDS.