Higher education is shifting from AI pilot projects to operational questions about learning, assessment and center for teaching and learning (CTL) roles. Experts forecast 2026 as the year institutions move from hype to measurable ROI for AI in administration and pedagogy; leaders must now define guardrails and governance. Teaching and learning directors report that CTLs are central to responsibly scaling AI tools, from assessment integrity to faculty development. Faculty conversations focus on practical deployment—how AI augments coaching, how oral exams or agent‑augmented workflows preserve learning outcomes, and how retrieval‑augmented generation (RAG) and model governance influence institutional risk. Instructional leaders should pair AI pilots with evaluation frameworks tied to student learning outcomes, data privacy and academic integrity; CTLs can lead this work by running small, measurable implementations before campus‑wide rollouts. Technical term: RAG (retrieval‑augmented generation) means linking large language models to curated institutional content so outputs are grounded in verified sources — a common approach to reduce hallucinations in campus AI tools.