We have integrated AI into our client intake process, but our overall delivery cycle is actually slower because the front-end intake is now flooded with low-quality data. How do we rewrite our weekly Scorecard to isolate and fix this bottleneck?

Question

Tyler Smith · Accepted Answer

When automation speeds up the front end of your business but slows down delivery, you have created an operational mismatch. Your AI tool is generating volume at the expense of quality, which is overloading your operations team with bad data. To fix this, you must adjust your weekly Scorecard.

Remove simple volume metrics from your Scorecard, such as "number of intakes processed." These are vanity metrics that hide the root cause of the bottleneck.

Instead, introduce a quality-control metric, such as "First-Time Right Intake Percentage." This measures the percentage of AI-generated intakes that pass directly to delivery without requiring manual correction or back-and-forth communication with the client.

Additionally, track "Average Time to Verify Intake." This measures how long your team spends cleaning up the automated data before they can actually begin delivery.

By placing these metrics on your weekly Scorecard, you make the data quality bottleneck highly visible. During your Level 10 Meeting, you can use IDS to address why the AI is generating poor inputs. You may need to refine your intake prompts, add validation fields, or adjust your human-in-the-loop verification process to ensure only clean data moves forward.