This year, generative artificial intelligence (AI) has emerged as a dominant theme across higher education, making its presence felt in nearly every corner of university operations. It was a recurring topic at major business officer conferences, reflecting its growing relevance and impact. As many institutions face budget constraints and announce workforce reductions,¹ the pressure to operate more efficiently with fewer resources has intensified.

Generative AI is no longer a futuristic concept; it’s becoming a foundational tool across university campuses. From administrative efficiency to student success and donor engagement, institutions are finding creative ways to harness AI’s capabilities. While early adoption was met with skepticism, especially in academic circles, the tide is turning as universities recognize the long-term value of integrating AI into their operations.

In the early days of generative AI, faculty members were understandably wary. Tools like ChatGPT and other AI writing assistants were often associated with academic dishonesty, enabling students to bypass learning by generating essays and solving assignments with minimal effort.

However, the narrative is shifting. Many universities now recognize the potential of generative AI to enrich learning rather than undermine it. Faculty are integrating AI into coursework² to teach students how to critically evaluate AI-generated content, use it responsibly in research and understand its implications in their future careers. New programs and certifications focused on AI literacy are emerging, preparing graduates to thrive in an increasingly AI-driven workforce.

Admissions offices have been among the earliest adopters of generative AI. Chatbots powered by AI are now common on university websites, answering questions from prospective students 24/7, guiding them through application processes and providing personalized support.

While these tools improve accessibility and responsiveness, their use in reviewing applications remains controversial. Concerns about bias and fairness persist, especially when AI is used to evaluate essays or recommend candidates.³ Still, AI can assist admissions officers by organizing and summarizing thousands of applications, helping them identify patterns and flag noteworthy submissions for deeper review.

For current students, schools are using AI to identify at-risk individuals based on behavioral and academic data. These systems can trigger early interventions, such as outreach from advisers or tutoring services.

As these models learn from more data, their accuracy and effectiveness are expected to improve significantly.

University finance departments are increasingly turning to generative AI to enhance compliance, streamline operations and reduce manual workloads. One of the most impactful applications is using AI to review expenses more efficiently. Rather than manually auditing thousands of transactions, AI systems can be programmed to flag anomalies—such as duplicate charges, unusual vendor activity or out-of-policy purchases—for further investigation. This not only saves time but also improves accuracy and consistency in financial oversight.

Another emerging use case is the deployment of restricted funds. Universities often manage grants and donations with specific terms and conditions. Generative AI can interpret these requirements and ensure that funds are allocated appropriately, reducing the risk of noncompliance and freeing up staff from tedious manual reviews.

AI is also transforming contract management. Tools can draft, review and compare contracts. These systems help ensure consistency in language, flag potential risks or discrepancies, and accelerate the procurement process. This is especially valuable for institutions managing hundreds of vendor relationships, research agreements and service contracts.

Together, these applications are helping finance teams shift from reactive oversight to strategic financial planning—reducing risk, improving transparency and allowing staff to focus on higher-value work.

Facilities teams at universities are using generative AI to enhance the way they manage campus infrastructure. One of the most promising applications is in predictive modeling for utilities. By analyzing weather data and historical usage patterns, AI systems can forecast energy consumption with remarkable accuracy. This allows institutions to optimize heating, cooling and electricity usage, reducing costs and improving sustainability across campus operations in the process.

In addition to energy management, AI also helps support deferred maintenance planning. Facilities departments often face the challenge of aging infrastructure and limited budgets. Generative AI models can analyze historical maintenance records, equipment life cycles and usage trends to predict when systems are likely to fail. This enables teams to schedule repairs proactively, allocate resources more effectively and avoid costly emergency fixes.

Together, these AI-driven strategies are helping universities move from reactive maintenance to predictive planning, thereby improving operational efficiency, extending the life of campus assets and supporting long-term sustainability goals.

This year’s NACUBO conference featured a discussion about a compelling use case: leveraging generative AI to assess donor potential. By analyzing internal data—such as past giving history and campus involvement—alongside external sources like public records and social media, AI could help development teams identify high-potential donors and tailor outreach strategies.

While promising, this approach raises important questions about privacy and data ethics. Universities should tread carefully, ensuring transparency and consent while using AI to support fundraising efforts.

Generative AI is no longer a fringe technology; it’s becoming a foundational tool in higher education. As universities continue to explore its capabilities, the focus is shifting from fear to opportunity. By embracing AI thoughtfully and ethically, institutions can enhance their operations, support students and staff, and better prepare graduates for the future.

Note: This article was written with the assistance of generative AI. All the ideas and topics came from conferences, news articles and conversations with university officials.

_{1 https://www.insidehighered.com/news/business/cost-cutting/2025/11/07/october-brought-deep-cuts-multiple-campuses?utm_source=Inside+Higher+Ed&utm_campaign=646290b088-DNU_2021_COPY_02&utm_medium=email&utm_term=0_1fcbc04421-646290b088-236153509&mc_cid=646290b088&mc_eid=dfd7e9f149}

_{2 https://www.insidehighered.com/news/student-success/college-experience/2025/10/29/podcast-teaching-alongside-generative-ai-student}

_{3 https://www.aera.net/Newsroom/Study-Algorithms-Used-by-Universities-to-Predict-Student-Success-May-Be-Racially-Biased}