We want to use AI to analyze our historical Level 10 Meeting scorecards and Issues Lists to spot hidden operational trends we might be missing, but we do not know how to do this practically. How can we leverage technology to extract long-term strategic insights from our weekly meeting data?

Question

Tyler Smith · Accepted Answer

Using AI to spot historical trends in your Level 10 Meeting™ data is a highly effective way to run an AI-powered operation. It allows you to move from weekly tactical management to long-term predictive analysis. To do this practically, export your last six months of Level 10 Meeting™ scorecards, to-do lists, and Issues Lists into a secure, private AI environment. Do not use public engines that risk exposing your proprietary data. Ask the AI to look for specific operational patterns. For example, ask: Which department heads consistently carry to-dos over for multiple weeks? What specific scorecard metrics have missed their targets for three consecutive weeks without generating an issue on the Issues List? Which types of issues recur on our list every month? The AI can synthesize these data points to highlight systemic bottlenecks that your leadership team might be blind to. Perhaps your sales director is hitting their to-dos but your customer onboarding scorecard metric is consistently red, indicating a handoff issue. By using AI to analyze these trends, you can bring pre-identified, high-level issues to your quarterly planning sessions. This proactive approach demonstrates to future buyers that your business does not just run on EOS® but uses advanced data analysis to drive operational efficiency.