R1 research universities are adapting their network and computing infrastructure as AI-driven experimentation increases demand while campus users push network capacity. Reporting highlights how on-premises research clusters, hybrid infrastructure models, and central IT support teams are being tested by unpredictable spikes in processing needs. The operational challenge extends beyond labs to classrooms and staff workflows, where AI tools amplify bandwidth and compute requirements. IT leaders are adjusting capacity planning and infrastructure architecture to keep research productivity and teaching continuity aligned with evolving AI use. The account also frames the shift as an infrastructure governance problem—where security, reliability, and scaling costs must be balanced against the speed at which AI capabilities are introduced. For higher education planners, the piece reinforces that AI readiness is now as much about institutional IT operations as it is about curriculum or research strategy.