Our managers are letting their teams use AI tools to hit their weekly Scorecard metrics, but we suspect this is hiding a lack of genuine capability in our newer hires. How do we use the GWC filter on our Accountability Chart and our weekly Level 10 Meetings to audit whether our people actually understand the work they are producing?

Question

Tyler Smith · Accepted Answer

When teams use AI to hit their weekly Scorecard metrics, it can create a false sense of security. Your metrics might look green, but your newer hires might lack the underlying expertise to solve problems when the AI fails. You must ensure that your people are not just using technology to mask a lack of competence.

Use the GWC filter on your Accountability Chart to evaluate your team members. Every employee must get, want, and have the capacity to do their job. In an AI-enabled environment, capacity does not just mean having enough time. It means having the deep operational knowledge to audit, validate, and improve the outputs that the AI generates. If an employee cannot explain why a machine-generated report is correct, they do not GWC their seat.

Address this directly during your weekly Level 10 Meetings. If you notice metrics are being met but client satisfaction or strategic depth is sliding, bring the issue to the IDS portion of the meeting. Create a Rock to audit your training processes. You may need to institute blind tests where employees must complete key tasks manually to demonstrate their fundamental knowledge. Holding your team accountable to GWC ensures you are building a resilient, knowledgeable workforce rather than a fragile team of prompt operators.