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Point Solutions Are Only the Start for AI in Grants and Research

Artificial intelligence will make research management smarter, easier to use, and more valuable — enabling quicker, better decisions for both researchers and administrators.

Search on "AI in academic grants and research" and you'll come across a bevy of point solutions. Some tailor generative AI to help with the challenges of developing and writing grants (see,, Others harness AI to speed the production of or improve the quality of academic reports themselves (Elicit, Paperpal, Scite).

But the real power of AI in academic grants and research will be in its fast-developing ability to strengthen and streamline academic research management from individual projects on through to the billion-dollar research portfolios of the world's most prestigious institutions.

AI will indeed sharpen grant-development and research-related processes. But more importantly, it will boost visibility into individual projects as well as across the full spectrum of a university's research activities. That, in turn, will enable better decisions and quicker action from principal investigator to university president. In short, AI will make research management smarter, easier to use, and more valuable to research leaders and their institutions.

That said, research-management systems are far from ubiquitous. Universities with nine-figure research budgets are still trying to manage it all using combinations of spreadsheets and decoupled solutions. That's increasingly risky, and AI is a big reason why.

Landing the Right Fish

AI will insinuate itself – and make life easier – across the spectrum of research planning and execution, research analytics, project and portfolio management, grants management, and institutional financial performance.

Pre-award, these systems can help match investigators with potential funders in powerful new ways. Rather than relying on past relationships or manually scraping the web for possible matches, AI's ability to take countless variables into simultaneous account can yield broader, higher-probability funding searches. Such matchmaking could incorporate the long-term track records of both funders and investigators as well their ambitions and how well they align. These systems could also suggest collaborations within or outside of an investigator's institution to boost the odds and expand the proposed effort's scope to better meet funder goals. AI-guided grant-writing solutions are already helping investigators tailor proposal language and approaches to the tastes of particular funders – say, the National Institutes of Health – based on training data sets that include vast numbers of winning proposals past. Given the time savings and competitive advantages they confer, such tools will soon be indispensable.

Research-management systems hit their stride when projects get rolling, and AI isn't changing that. These systems already keep close tabs on outlays to avoid over- and underspending. They follow money and time across departments so sponsors can see how their dollars are being spent. They manage compliance with institutional and funder-related rules and milestones. And they present it all in easy-to-digest reports and dashboards that help research leaders make better decisions on a multitude of fronts.

Budgeting and Forecasting Are Early AI strengths

AI's influence on research management will first be most obvious in the areas of research planning, administration, and forecasting. AI-driven research-management systems will turn successful grant proposals into draft project plans with a few keystrokes. Along the way, they'll create automated job postings for the research assistants, postdocs, and others critical to the project's success.

Budget forecasting that combines AI and advanced regression models promises to deliver high-fidelity products with minimal human effort. As budgets and actuals diverge, AI-based systems will adjust forecasts and suggest ways to get back on track, be it through targeted spending trims or budget transfers.

With time, that forecasting will extend to specific research tasks as AI mines the details of past programs to predict the course of current and future efforts. Throughout, research teams will interact with AI copilots that help solve problems quickly through natural language queries whose answers will, with time, also derive from AI.

For academic leadership, AI-powered research management promises greater transparency and deeper insight into an institution's overall research portfolio. One example: With real-time visibility into grant proposals' status and success rates, research VPs and others can spot emerging areas of strength to be fostered – and also places where flagging success might call for extra guidance in crafting proposals that appeal to funders' ambitions and risk tolerance.

More than Point Solutions

AI will ultimately bring much more to the academic research enterprise. This is an enabling technology. Creative minds with problems to solve will exploit it in unexpected ways.

Software developers aren't waiting around for inspiration to strike. They're running focus groups with higher education and research customers to understand how to most strategically apply generative and other AI to address the pain points of the research enterprise.

Success in academic research is more important than ever. It boosts institutional rankings and reputation, attracts students and faculty, and drives revenues. The benefits of harnessing AI-powered research management are too great for academic institutions to ignore. The point solutions that today's web searches drum up are only the beginning. AI in research management will make research teams and their institutions more productive and effective. Universities, researchers, and society as a whole will benefit.

About the Author

Rob Jonkers is global senior director of Higher Education & Research at SAP.

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