Tactical and Strategic View on the Impact of AI for Research Administrators
Two views on how Artificial Intelligence technology can help to make research administration more efficient in the near-term and more strategic in the future.
In the domain of grant management, there is significant concern over the impact that broad access to Artificial Intelligence (AI) tools will have on the quality and quantity of grant submissions. There will certainly be a period of adjustment where grant submission requirements will adapt to the new reality. The same technologies will also impact the research administrators (RA) role. To better understand how the technology may be used, it is helpful to look at the near-term tactical view as well as a longer-term strategic perspective.
One way to view the impact that Large Language Model (LLM) derived AI tools will have on work is to consider that they can change the balance between what is “easy” and what is “hard”. In a traditional workflow, reading through many documents and ranking them based on relevance to some topic of choice has historically been a hard and tedious task. With an LLM-based AI, this type of ranking can be largely automated. When added to the workflow of a busy RA, this capability can be transformative. For example, such a system could free up significant time that was previously spent reviewing and classifying new grant opportunities. That time could be shifted toward providing personalized support for each researcher. This may sound futuristic, but this is the type of tactical capability that an office of research should be looking for as they evaluate next-generation tools to support their work.
When considering the longer-term, strategic impact of this technology, it is useful to step back and consider not just support for an individual researcher, but the development of research departments and programs. Experienced RAs know that there is a competitive nature to applying for and winning grants. Funding agencies have limited resources and must be selective when choosing recipients of awards. Winning a larger share of grants requires a long-term strategy to build up capabilities and a track-record of results. Developing a long-term strategy while considering how other institutions will likely adapt and respond over time is effectively an application of game theory. This level of industry expertise is beyond the reach of current AI models, but we can see early signs of evolution. Today, there are examples of AI-based systems that can beat the best human competitors at complex games like Chess and Go. Combining these learning algorithms with industry knowledge is the next step in the evolution of AI tools that will help institutions make better strategic decisions.
These are just two examples of how AI technology can enable RAs to be more effective at supporting a community of researchers. In both cases, the impact of AI is to change the definition of what is easy and what is hard, allowing the human decision makers to be far more effective at achieving their goals. Integration of AI in the research funding and planning process can achieve better alignment between the capabilities of the institution and the goals of funding organizations to ultimately achieve better research outcomes for the institution and for society.
Authored by Brian Romansky, Chief Strategy Officer
Profound Impact
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