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When AI Pilots Show Promise, But Outcomes Stay Flat 
By Mickey Baines. As institutions begin exploring new approaches to better interpret and act on student signals, AI is often one of the first places they turn. In many cases, that exploration starts with pilots. Teams begin applying AI to specific parts of their work—drafting and refining email and text campaigns, testing subject lines, responding to inbound student questions more quickly, or summarizing student records to prepare for outreach. Advisors and student support teams use it to triage common questions, identify next steps for students who have stalled, or capture and organize notes from prior interactions. Leadership teams use it to analyze large data sets, surface trends, and move more quickly from information to decision.
When More Data Makes It Harder to Know What Matters 
By Mickey Baines. As enrollment teams increase the volume of outreach, engagement, and analysis across the funnel, the challenge is no longer access to data—it is understanding what that data actually means. Institutions today can see more of the student journey than ever before. Email engagement, website activity, event attendance, application progress, and interactions across multiple systems all contribute to a growing pool of observable behavior. In theory, this level of visibility should make it easier to understand intent, prioritize efforts, and guide students more effectively toward enrollment.
When More Activity Doesn’t Lead to More Clarity 
By Mickey Baines. At this point in the enrollment cycle, most institutions are not struggling with a lack of activity. In fact, the opposite is often true. Outreach has increased, engagement efforts are expanding across channels, and application volume in many cases is holding steady or growing. Teams are working at a faster pace, supported by more data, more tools, and more visibility into student behavior than at any point in recent memory.

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