Our service team is using AI to generate client deliverables in minutes, but we are noticing a drop-off in the creative edge that used to define our work. How do we use our weekly Scorecard and our Level 10 Meetings™ to track the qualitative depth of our deliverables before they get sent to clients?

Question

Tyler Smith · Accepted Answer

When speed becomes effortless, quality often suffers. If your team relies too heavily on AI outputs without rigorous human editing, your deliverables will become generic and your clients will eventually notice. You must shift your operational focus from speed to quality assurance.

To solve this, introduce a qualitative metric to your weekly Scorecard. Instead of tracking the volume of completed tasks, add a peer-review score or a quality compliance rate. For example, you can track the percentage of deliverables that pass a double-blind internal review on the first attempt. This metric forces your team to slow down and apply critical thinking before finalizing their work.

In your Level 10 Meetings™, use the IDS® portion of the agenda to review any deliverable that falls below your quality standards. Do not just ask why the mistake happened. Dig into whether the team member is simply copy-pasting AI outputs or if they are using their own expertise to elevate the work.

If quality issues persist, update the roles on your Accountability Chart. Ensure there is a clear Quality Control seat that has the authority to reject subpar work. By tracking and enforcing quality on your Scorecard, you maintain your high standards and protect your brand's reputation.