Generative AI is causing enormous disruption across academia. Faculty, researchers, staff, and students are now at the crossroads of opportunity and risk, as AI tools rapidly change the way they work, learn, and collaborate.

A key challenge is protecting sensitive data. Universities must ensure that research, intellectual property, and sensitive information are not unintentionally exposed through the use of AI systems, which could inadvertently feed confidential data into external models. Staff also need to be transparent with students about how their data is being collected, used, and possibly shared by AI applications—clear communication is vital for maintaining trust.

Meanwhile, shadow AI use—where staff and students adopt generative AI tools informally, outside of approved IT policies—presents a growing governance issue. As AI-enabled applications multiply, many users are exploring them without fully understanding their ethical implications, data security risks, or the biases these systems might embed. With no defined institutional standards, staff are left to navigate this landscape on their own, often unsure of the best practices for integrating AI responsibly into their work.

Successfully embracing AI will require staff to be proactive in understanding how these tools fit into the university’s evolving ecosystem. But many feel uncertain, asking: "How do we engage with AI in ways that are both innovative and ethical?" The answer lies in developing clear policies, fostering open dialogue, and providing staff with the guidance and training they need to confidently lead through this paradigm shift.