Higher-education leaders at the ASU+GSV Summit warned that AI’s benefits in student employability are arriving alongside a major operational risk: the speed at which faculty must redesign courses. Multiple presidents emphasized that course redevelopment cycles of three to five years may no longer be viable, and workload models may need to change to keep curricula current. At the same time, new research from Pearson and Amazon Web Services reported an employer-readiness mismatch. Employers said 53% of the time their biggest challenge was finding graduates with the right AI skills, while only 28% of higher-education leaders’ counterparts said universities were keeping up with AI-driven change. The studies also point to compliance and confidence gaps among students: while many students use AI for core academics, only a small share reports high professional-setting proficiency, and fewer feel their use aligns with institutional policies. Together, the developments frame AI readiness as both an academic workload and workforce alignment problem—one that affects curriculum governance, academic policy on acceptable AI use, and how institutions build applied learning pathways with employers.