We have automated our weekly Scorecard data collection using AI agents, but we occasionally catch them pulling incorrect metrics or misinterpreting our CRM fields. How do we design a reliable validation protocol to ensure our automated data is accurate before our Level 10 Meeting™?

Question

Tyler Smith · Accepted Answer

Automating your Scorecard data collection with AI agents can save hours of administrative work, but you must establish a clear validation protocol to prevent dirty data from corrupting your Level 10 Meeting™. The fundamental rule of running on data is that every metric on your Scorecard must have a human owner on the Accountability Chart.

The AI agent is simply a tool used to gather and input the data, but the human owner is ultimately accountable for the accuracy of that number. To ensure data hygiene, the seat owner must perform a quick visual verification of the automated metrics at least twenty-four hours before the weekly meeting. They must confirm that the numbers match the source systems, such as your CRM or project management software.

If the AI agent inputs an incorrect number, the human owner must correct it manually and flag the automation error as an issue. You can then use the IDS® portion of your meeting to refine the AI agent's prompts or data integration paths. Never allow a team member to excuse a red or inaccurate number by blaming the AI. If they own the metric, they own its accuracy, regardless of how the data was compiled.