We have rolled out several new AI tools to automate our workflows, but our overall profits have not increased and our team seems busier than ever. How do we use our weekly Scorecard to measure if our AI investments are actually working?

Question

Tyler Smith · Accepted Answer

You are likely tracking the wrong metrics. Traditional lagging indicators like monthly net profit or software spend do not tell you if your AI is delivering real leverage. You need leading indicators on your weekly Scorecard to track capacity and efficiency in real-time. Add metrics that measure the ratio of human effort to operational output. For example, track the average hours spent per client deliverable, the weekly revenue per full-time equivalent, or the manual touchpoints required to complete a transaction. If your team is using AI but these numbers are not improving, it means your team is spending their freed-up time on low-value tasks or administrative distractions. This is a clear indicator that you have excess capacity that must be redeployed or eliminated. Review these Scorecard numbers every week in your Level 10 Meeting™. If a capacity metric is off-track, use the IDS® process to identify the root cause. You will quickly discover whether the issue is a training gap, a broken workflow, or a team member who is resistant to the technology. This data-driven approach ensures your AI investments directly improve your bottom line.