Investment in research is critical to a healthy, prosperous society. Researchers bring creativity, curiosity, and critical thinking to push the boundaries of knowledge for the benefit of society. Being a researcher is both rewarding and tough. They must decide what to work on, plan how to do it, find people to collaborate with, write papers, share their contributions with the community, secure funding, attract talent, and manage administrative work—all at the same time. These tasks can consume 20 to 40 percent of a researcher's time before they even touch an experiment. This workload is compounded by the need to connect dots outside of their own area of expertise; taking on some of the biggest challenges facing society requires interdisciplinary approaches.
Artificial intelligence (AI) has the potential to help researchers get to better outcomes faster across a number of the tasks involved in their day-to-day work. However, in order to be research-grade, AI has to reinforce a researcher’s own creativity, be grounded in trusted data, and put researchers in the driver’s seat.
Reinforcing Creativity, Curiosity, and Critical Thinking
Researchers are intensely curious in the pursuit of new knowledge—wanting to understand the “what and why” behind the evidence that is in front of them, looking at different and conflicting perspectives on the same research question, digging into the reasons for the differences, and scrutinizing the approaches being taken before accepting any conclusion. That intense curiosity combined with the discipline to keep asking questions even when the conclusion appears self-evident or widely accepted has been a major contributor to progress.
General-purpose AI is remarkably good at producing fluent answers. For many everyday tasks, that’s enough. Research is different. Consider a research question and a claim supported by 10 published papers. At first glance, that seems persuasive. But what if all ten papers are actually editorial opinions, or they all originate from the same lab? Now consider another claim relating to the same research question but this time supported by three registered randomized controlled trials from different groups. Most researchers would weigh those bodies of evidence very differently.
The number of citations alone isn’t the point; the quality, diversity, and strength of the evidence is. Surfacing this helps researchers evaluate the output. Where did the evidence come from? How strong is it? What perspectives are missing? What research reaches a different conclusion? Those questions are not obstacles to research—they are the foundation of creativity, curiosity, and critical thinking that fuel it.
Grounding AI Models in Trusted Content and Data
Delivering on this vision requires more than choosing the latest language model. Models will continue to improve, and new ones will inevitably emerge. What sets research-grade AI apart from general-purpose AI is not the underlying model, but the layers of research knowledge, evidence, and human expertise used to guide and ground it.
Research-grade AI should be grounded in trusted content and data, such as peer-reviewed literature, registered clinical trial results, citation and provenance records, and structured metadata about researchers, institutions, and funding. This information should be organized in a way that helps researchers understand relationships across research, people, institutions, publications, funding, and ideas.
Putting Researchers in the Driver’s Seat
Research-grade AI requires the active participation of the research community—both in exercising judgment based on the strength of the evidence for any given task and in providing feedback for the improvement of AI. Continued input from researchers is essential to ensuring AI evolves alongside research practice.
The purpose of research-grade AI is not to remove uncertainty from research because research has always advanced through the scientific method: questioning accepted ideas, testing competing hypotheses, and refining what we know. Meeting the "research-grade" standard depends on pairing trusted, well-curated content and data with ongoing input from the researchers who use these tools every day. That combination, rather than any single model or feature, is what allows AI to earn a durable place in how discovery gets done.
