Data-Rich to Information-Rich: AI Trends in Higher Education Institutional Research

Higher education institutions have long been data-rich, accumulating vast datasets across enrollment, academic records, financial aid, and student success. Yet many IR teams face challenges in turning those datasets into timely, actionable insights—not due to lack of expertise, but because a significant share of their time is devoted to maintaining data systems and producing reports. New developments in AI—from natural language querying to intelligent agents are beginning to change that balance, easing data-heavy workflows and freeing more time for analysis and strategic advising. As these tools help shift the focus from maintaining data and documentation to transforming information into actionable intelligence, IR professionals can expand their impact on institutional decision-making. This transition is being accelerated by modern data platforms and the growing integration of AI. In this article, we explore eight emerging trends that show how AI is helping IR offices evolve from data-rich to information-rich, and the practical steps institutions can take to prepare for this shift.

1. From Experimentation to Integration AI tools such as generative AI, chatbots, and predictive analytics are moving from trial phases into essential infrastructure supporting enrollment management and institutional decision-making. Institutions are beginning to embed these tools in operational processes rather than running isolated^1-3.
2. Enthusiasm vs. Execution While AI’s promise is widely recognized, national surveys, including the 2025 UPCEA-EDDY report², show that many campuses are still building the strategies, technical readiness, and staff capacity needed to integrate it effectively into IR workflows. Successful adoption depends on pairing enthusiasm with structured plans, adequate resourcing, and staff development.
3. Data Infrastructure as a Foundation Reports from EAB highlight that legacy systems, siloed data, and minimal governance often limit how far institutions can go with AI¹. Strengthening integrated, accessible data environments enables IR offices to shift more time toward analysis, insight generation, and strategic advising.
4. Rise of Unified Data Platforms Institutions implementing thoughtful AI strategies are already reducing reporting backlogs, improving data literacy, and empowering non-technical users through natural language querying. Case studies shared in 2025 Institutional Research forums such as NJAIR 2025, and AIR Forum 2025 show these unified platforms are transforming how data supports academic and operational planning⁴.
5. AI Agents and Intelligent Workflows Early adopters are experimenting with AI agents to assist in both administrative and academic workflows—from monitoring enrollment patterns to streamlining course planning. By taking on repetitive monitoring and reporting tasks, these AI tool free IR teams to focus on higher-level analysis and strategic priorities.
6. Uneven Maturity Across Institutions While many institutions are still in the early stages of AI and data maturity, discussions at 2025 IR conferences, forums, and webinars revealed promising cases where institutions are moving ahead—aligning tools, governance, and staff development together to create the foundation for AI-enabled decision-making at scale^4-8.
7. Common Barriers and How to Address Them AI adoption in IR is shaped by a set of shared challenges, which can be addressed through deliberate planning:
   • Budget and Infrastructure: Budget constraints remain one of the most cited barriers, with many institutions still operating on legacy systems not designed for integration. These outdated platforms often create fragmented data environments and “architecture by accident.” Phased investments, cloud-based solutions, and partnerships can help modernize infrastructure without overwhelming budgets.
   • Governance and Security: Concerns around data privacy, inconsistent definitions, and uncoordinated governance structures can undermine AI adoption. Establishing clear data governance policies, role-based access controls, and transparent AI usage guidelines build trust and safeguards sensitive information.
   • Skills and Culture: Limited staff readiness, coupled with resistance to change, can slow AI uptake. Encouraging professional development, offering targeted upskilling, and creating space for small-scale AI pilots help teams adapt and gain confidence with new tools.
   • Integration and Interoperability: Unmanaged APIs, siloed systems, and on-premises data storage can limit AI agents’ effectiveness and hinder automation. Coordinating integration strategies and investing in interoperable systems ensures AI tools can work across institutional platforms.
   • Strategic Alignment: Without a cohesive institutional plan, AI initiatives risk becoming disconnected from mission priorities. Embedding AI projects into strategic and operational plans ensures they address critical goals, improve cross-unit collaboration, and gain stakeholder buy-in.
8. Strategic Imperative to Act Leaders across higher education agree: investing in AI readiness positions an institution to enhance competitiveness, improve efficiency, and better serve students. For IR, the opportunity lies in moving from managing data to serving as an information hub—leveraging AI to streamline routine work and focus on insights that guide institutional strategy. Institutions that take deliberate, well-governed steps now will be best positioned to lead in this new, information-rich era.

References

[1] Doci, C., & Waldo, L. (2025, July 14). How to Prepare Your Campus Ecosystem for an AI-Powered Future [Webinar presentation]. EAB.

[2] Lava, S. (2025). API First: How AI and AI Agents Will Shape Integration and API Management Strategies [Analyst brief]. IDC, Sponsored by Salesforce.

[3] West, E., Sullberg, D., McGee, E., Russell, S., & Kim, N. (2025). Marketing and Enrollment Management AI Readiness Report 2025: Benchmarking Emerging Technology Adoption in Higher Education. UPCEA & Education Dynamics.

[4] Zhang, X., Dinh, D., & Deess, E. (2025, July 28). Designing an AI-powered IR chatbot to foster cross-departmental collaboration and data access [Conference presentation]. NJAIR 2025 Annual Conference (Virtual), New Jersey Association for Institutional Research.

[5] Lin, Y. (2025, May). Impacts of AI on higher education and institutional research [Discussion group]. 2025 AIR Forum, Orlando, FL, Association for Institutional Research.

[6] Yang, E. (2025, May). Enhancing data privacy in the age of AI/ML in higher education [Speaker session]. 2025 AIR Forum, Orlando, FL, Association for Institutional Research.

[7] Dodson, B., & Gross, B. (2025, May). How holistic data integration is transforming higher education [Panel session]. 2025 AIR Forum, Orlando, FL, Association for Institutional Research.

[8] Alapati, S., & Vijayakumar, B. (2025, April). Using AI to improve data accessibility [Speaker session]. 2025 NJAIR Conference (Virtual), New Jersey Association for Institutional Research.