Driving Research and Innovation with AI

This chapter presents a practical roadmap for using artificial intelligence (AI) to elevate university research from isolated, manual workflows to coordinated, high-impact discovery. It begins by positioning AI as a catalyst that complements human expertise, shortening the path from hypothesis to publishable result while strengthening cross-disciplinary collaboration. The chapter then outlines how to select high-impact pilots that align with faculty strengths and institutional priorities, followed by an innovation-focused readiness assessment covering compute capacity, data governance, skills, collaboration structures, and ethics. A research-specific pilot framework translates these ideas into action—scoping and approvals, team formation, technical setup, iterative execution, and evidence-based decisions to scale, pivot, or conclude. To track impact, a research scorecard moves beyond publication counts to measure speed and efficiency, quality and depth, interdisciplinary reach, partnerships, researcher development, and compliance. Guidance on mobilizing early adopters, building AI hubs, and forging external funding and industry partnerships shows how momentum spreads across campus. The chapter also addresses common hurdles—over-automation, data fragmentation, and talent gaps—with governance, training, and transparent documentation. Looking ahead, it explores emerging capabilities (e.g., large-scale generative models, quantum methods, lab automation) while reaffirming the central role of human judgment. Throughout, concise examples (climate modeling, protein-guided experiments, cultural archives, and policy forecasting) illustrate pathways from pilot to enduring research excellence.