Colleges are moving faster on AI, but a core operational risk is emerging: systems are generating value in isolation while student and administrative data remain fragmented. One AI platform CEO warns that without “connected” data across an institution’s 80–100 separate systems, AI deployments can become “bolt-on” automation that misses the moment when student needs change mid-semester. A related higher-education perspective argues that the practical learning benefits of AI depend on human instruction design, not just tools. The discussion highlights the limits of classroom technology that offloads busywork without replacing real coaching, mentoring, and expectation-setting. Researchers and leaders are pushing for deeper instructional use cases rather than passive experimentation. Separately, new reporting points to student behavior: learners are adopting AI for support but are increasingly concerned about false accusations of misuse. Together, the pieces suggest universities face a dual challenge—aligning AI with pedagogy while also establishing fair, transparent misuse-detection and academic integrity processes. Finally, the broader campus implementation question is shifting toward workforce and student-success workflows. The emerging emphasis: using AI to reduce enrollment friction and strengthen retention, while coordinating across IT and analytics functions so decisions can follow students in real time.