How AI improves research management in higher education
AI will make research management smarter, easier to use, and more valuable to research leaders and their institutions.
U.S. federal scientific research funding is under siege, and higher education is feeling the pain.
National Science Foundation grant awards have slowed to a trickle. NASA science funding is under threat. Defense research isn’t immune, either. The biggest hit looks to be in health-related research, with the National Institutes of Health capping reimbursements for indirect costs at 15% (indirect costs can run a multiple of that percentage).
In fiscal 2023, the NIH spent about $9 billion on such costs, which help cover facilities and staff time critical to research projects. That’s roughly the same as the NSF’s total grantmaking capacity.
Research universities are scrambling to adapt. They’re involved in lawsuits. They’re working through associations to advocate for their research endeavors and, in the case of the NIH indirect cost cuts, to highlight how vital indirect expenditures are to frontline research. They’re delaying outlays, freezing hiring, and rescinding graduate-student admissions. They’re considering ways to diversify their funding channels.
More broadly, they’re looking hard at their financial and operational models. It’s here that technology can play an indispensable role in helping universities make the best of a rough period for American scientific research.
Lean on core tech to manage scientific research funding cuts
The core technologies—ERPs for higher education and research—are often in place already. Broadly speaking, ERPs help universities streamline operations, optimize resource allocation, and provide real-time operational and financial insight across departments.
Analytics solutions combining advanced regression models and, increasingly, artificial intelligence tapping into ERP data, have never been more important.
Take the example of NIH indirect cost caps: analytics can help forecast budgets, track cost recovery, and predict future funding gaps. And they can do this for a wide range of scenarios—that’s key in an environment where everything from best-case to worst-case seems more plausible than ever.
Such scenarios might combine:
- Varying levels of federal scientific research funding and cost recovery.
- Alternative revenues from industry partnerships, fundraising campaigns focused on research support, or reallocated/newly allocated endowment money.
- Cost-sharing/facilities sharing with other institutions or consortia.
- The possible reallocation of funds previously dedicated to capital projects that may now have to wait.
- Differing research priorities based on varying indirect-cost impacts. For example, studies that require a dedicated metabolic chamber or a radiopharmaceutical customized in a medical cyclotron might temporarily cede priority to observational or pharmaceutical studies with lower indirect costs.
Put grants management systems to work
In addition to core ERPs for higher education and the analytics solutions those ERPs directly or indirectly power, university research leaders and administrators should focus on getting the most out of their grants management systems.
These systems, which cover the full lifecycle of grant funding, from proposal to closeout, can:
- Ensure compliance with federal regulations as well as institutional and funder-related rules and milestones.
- Track financial inflows and expenses, and justify and maximize allowable cost reimbursement.
- Keep close tabs on outlays to avoid over- and underspending.
- Follow money and time across departments so sponsors can see how their resources are making a difference.
- Present it all in easy-to-digest reports and dashboards.
AI is playing a growing role in these systems. Its ability to combine financial, operational, and other data from across research portfolios boosts transparency and provides insight into what’s working and what’s not across the institution’s research landscape.
Real-time visibility into grant proposals’ status and success rates reveals areas of strength to be cultivated—as well as areas that need boosting.
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Scientific research funding cuts: Tapping AI for help
It’s not all about efficiency and optimization, either. Research management systems augmented with AI can boost proposal success rates by helping match investigators with potential funders in powerful new ways.
Rather than relying on past relationships or scraping the web for possible matches, AI’s ability to take countless variables into account can yield better matches and higher hit rates in funding searches.
AI can consider the long-term track records of both funders and investigators, as well as their respective interests and ambitions, and assess how they align. Grants management AI can also suggest collaborators to strengthen grant proposals and help tailor the project’s scope and direction to a potential funder’s preferences.
Big decisions require the best data
Federal science funding is the seed corn of America’s future health and prosperity. It isn’t going away. But uncertainty on so many fronts is forcing university leaders to make decisions of enormous impact, quickly. The consequences of decisions made based on imperfect information threaten to be compounded by the current instability of the research-funding environment.
The technologies to improve information quality by distilling dizzying numbers of variables and crystallizing the best options are out there. Especially now, academic leaders can’t afford not to exploit them.
