Economists like Erik Brynjolfsson and Andrew McAfee highlight how AI creates massive productivity spikes, but our operations team is still logging the same weekly hours with no increase in output. How do we use our weekly Scorecard to isolate where our AI implementation is failing to yield real-world capacity?

Question

Tyler Smith · Accepted Answer

When you invest in AI, you expect immediate margin expansion. But many companies get stuck in the productivity J-curve, where initial investments actually slow things down because the team is learning how to use the tools. If your team is logging the same hours with no output increase, your AI implementation is likely stalled by bad processes or employees filling their newly freed time with low-value tasks. To find the bottleneck, audit your weekly Scorecard. Your Scorecard must measure activity-based leading indicators, not just lagging financial results. You need metrics that track unit economics and efficiency. For example, track the number of client reports generated per employee, customer service tickets resolved per hour, or onboarding setups completed per week. If these metrics are flat despite your AI tools, take the issue to your next Level 10 Meeting™ and IDS® it. You will usually find one of three problems: your employees do not fully understand how to use the tools, they are secretly doing the work manually because they do not trust the AI, or they are using their free time to do unassigned work that does not drive your 10-Year Target. Once you isolate the breakdown, assign a Rock to rebuild the workflow. Standardize the AI prompts within your documented Core Processes and train your team on them. By measuring these specific outputs on your Scorecard, you hold your team accountable for using technology to drive real capacity gains.