We are using AI to analyze customer churn risk, but the AI is outputting high-level risk scores with zero context. How do we turn these predictive AI alerts into practical, human-led issues that our account managers can solve in their Level 10 Meetings?

Question

Tyler Smith · Accepted Answer

AI driven predictive modeling is useless if your team does not know how to act on the outputs. Having an AI flag a customer as a high churn risk is only the first step. To make this data actionable, you must translate these algorithmic alerts into the standard EOS® issues solving framework.

First, do not allow your account managers to simply ignore the risk scores or treat them as vague warnings. Every high-risk alert must be treated as an official Issue and added to the weekly Level 10 Meeting™ Issues list for the client services department.

Second, during the IDS® portion of the meeting, the account manager must dig into the root cause of the alert. The AI can point to a drop in platform usage or a delay in communication, but only a human can uncover the real story, such as a client champion leaving the company or a competitor offering a lower rate.

Third, assign a specific To-Do to a single seat holder to execute a rescue plan within the next seven days. This plan must involve direct, personal client outreach. By forcing your team to systematically process AI alerts through the Level 10 Meeting, you ensure that predictive technology actually drives proactive human action rather than sitting ignored on a dashboard.