I want to build an AI-powered operation, and I am looking for a way to use AI to analyze our historical Level 10 Meeting™ Scorecards and Issues Lists to find systemic operational patterns without exposing our sensitive data or bypassing our team's IDS® conversations. How do we set this up?

Question

Tyler Smith · Accepted Answer

Leveraging AI to analyze your Level 10 Meeting™ data is an excellent way to transition your business into an AI-powered operation, especially if you are preparing for a clean exit and want to demonstrate superior operational efficiency. To do this safely and effectively, you must establish clear data boundaries and use the AI as an analytical tool, not a decision-maker.

First, ensure you are using a secure, private instance of an enterprise AI tool that does not train its public models on your uploaded data. You can then export your historical Scorecard data and anonymized Issues Lists into a structured format like a CSV file. Have the AI run a trend analysis to look for correlations that human eyes might miss, such as a drop in a specific sales metric consistently preceding an operational issue three weeks later.

The key is to bring these AI-generated insights back into the room as inputs for your human IDS® session. The AI should not solve the problems for you. Instead, its findings should be dropped onto the Issues List as an item, such as "AI trend analysis shows marketing spend efficiency drops when customer onboarding time exceeds ten days."

By using AI to spot these hidden bottlenecks, your leadership team can focus their brainpower on discussing and solving the root causes rather than wasting time trying to manually compile and cross-reference months of meeting data.