Research administration has grown increasingly complex over the past two decades, and the introduction of AI has only exacerbated this. Expanding regulatory requirements, shifting funding landscapes, rising numbers of proposals, and constrained staffing resources have made research administration more demanding. For emerging research institutions, these pressures compound existing structural challenges in building research operations capable of securing and sustaining funding. As generative AI tools have rapidly entered the market, accompanied by promises of increased efficiency, research offices at emerging research institutions, like those at more established institutions, have thus begun to explore whether, where, and how AI might reduce administrative burden and strengthen research administration capacity. Yet the stakes are high: limited resources and expanding expectations across the research enterprise, combined with uncertainty around the cost, risk, and return on investment of AI, make it difficult to determine how to productively integrate AI into workflows while still maintaining research data integrity, meeting the requirements of funders, and protecting budgets.

With support from the National Science Foundation’s GRANTED program, Ithaka S+R collaborated with Chapman University and Montclair State University to better understand how emerging research institutions are approaching AI in research administration and identify how they can leverage AI to increase their funding competitiveness. At two multi-institutional workshops held in 2025, we convened research administrators, librarians, IT leaders, and research development professionals to share experiences, identify challenges, and explore potential paths forward.

Today, we are pleased to share a new issue brief, AI Adoption in Research Administration at Emerging Research Institutions, that synthesizes insights from these workshops. The report details how institutions are experimenting with AI, where people in research administration are encountering barriers, and what early practices show promise for more effective and sustainable adoption.

Key findings

  • Data governance is foundational. Participants emphasized that data quality, security, and governance must remain central to any successful AI initiative. Fragmented data systems, evolving policies, and concerns about accidentally exposing sensitive information complicate implementation. Without stronger infrastructure and clearer governance frameworks, confident AI use is limited.
  • Trust and reliability are non-negotiable. Participants stressed the importance of transparency in how AI tools function and how decisions about their use are made. Given the high-stakes nature of research administration, many questioned whether current tools are sufficiently accurate and dependable for compliance-sensitive work.
  • Institutional ambition and operational reality are often misaligned. While leadership is eager to pursue AI initiatives, staff often lack clear guidance, sufficient resources, and realistic implementation plans. This contributes to fragmented, uneven use, driven more by individual initiative than coordinated strategy.
  • Workforce implications are unclear. Participants expressed some optimism about AI’s potential to reduce administrative burden, but concerns about how automation may reshape career pathways and the development of professional expertise were prevalent.
  • Promising practices are emerging. Currently, institutions are using AI to augment, rather than replace, human judgment, especially in risk-based review of proposals, contracts, and expenditures. Others are exploring uses in proposal development, collaboration discovery, and project analysis, while creating structured opportunities for staff to share ideas and develop practical use cases.

We are grateful to the workshop participants whose insights made this issue brief possible. Their willingness to share experiences, concerns, and successes reflects the collaborative approach that many see as essential to successfully integrating AI in the research enterprise.

This issue brief is part of a portfolio of work that investigates how AI is impacting the research enterprise across US and international contexts. As institutions consider whether and how to move from individual experimentation toward more intentional and systematic integration, questions around governance, workforce development, and cross-institutional collaboration will remain central. For more information on our work on AI in research administration, please contact Ruby.MacDougall@Ithaka.org.