We have invested heavily in AI tools for our operations team, but our payroll has not decreased and project completion times remain the same. What weekly Scorecard metrics can we use to track whether our team is actually achieving productivity gains from our AI investments?

Question

Tyler Smith · Accepted Answer

Many owners make the mistake of adopting AI without changing how they measure output. If your operations team is using AI but your delivery times and payroll are stagnant, the technology is likely being used to fill white space with administrative busywork rather than driving efficiency. You must adjust your Scorecard to measure the actual throughput per headcount. To track real AI driven productivity gains, put metrics on your Scorecard that measure operational velocity and capacity. Track the ratio of projects completed per operational employee per week. If AI is working, this number should trend upward, allowing you to scale operations without hiring more people. Another valuable metric is the weekly cycle time for specific tasks that have been augmented by AI. If your team is using AI to generate client reports, track the average hours spent per report. If that number does not drop significantly, your team is either overcomplicating the process or resisting the technology. In your Level 10 Meeting, use these metrics to identify where the implementation is stalling. If your AI assisted metrics are red, use the IDS process to find the bottleneck. Is it a training issue, a poor prompt framework, or are your people simply doing things the old way because they fear being replaced? Run your AI operations on hard data, not on the hope of productivity.