Artificial intelligence (AI) is transforming how we work, discover and solve some of our most challenging problems. At the University of Michigan, we’re not approaching this shift hesitantly – we’re going all in.
With more than $2 billion in annual research volume across 19 schools and colleges, U-M Dearborn and U-M Flint, U-M is one of the largest and most comprehensive research universities in the world. But scale alone isn’t enough. In today’s fast-moving world, we must be nimble, creative and collaborative to embrace new technologies like AI that help us tackle increasingly complex challenges.
AI isn’t just a tool. It’s a catalyst that is accelerating discovery, scaling research and unlocking insights that were once out of reach. That’s why U-M is making deliberate investments in infrastructure, talent and collaboration to integrate AI across our entire research enterprise.
“At the University of Michigan, AI isn’t just a tool. It’s a catalyst that is accelerating discovery, scaling research and unlocking insights that were once out of reach.”
At MIDAS, we see this transformation daily. From medicine and engineering to the arts and humanities, faculty are using AI to reimagine their research and amplify its impact.
Michigan Medicine clinicians are using AI models to predict patient outcomes, personalize treatment and detect early signs of deterioration, helping teams intervene earlier and save lives, and improving real-time clinical decision-making. Public health researchers use AI to anticipate disease outbreaks and model climate-driven infection trends. In a new ARPA-H–funded project, U-M researchers are developing an AI-guided mobile clinic to bring specialist support to rural providers nationwide.
In the physical sciences and engineering, AI is helping detect faint solar system objects; design new materials; predict natural disasters and build digital twins of cities, factories and even people. Environmental scientists are using AI to analyze satellite data, optimize energy systems and forecast the impacts of climate change. Biologists are designing AI-driven drug candidates to support species conservation.
But AI’s reach goes well beyond STEM. In literature and the arts, scholars use AI to translate complex texts across languages and cultures. Faculty are customizing learning and student support in the classroom. Economists and policy experts apply AI to study labor markets, poverty and misinformation with unprecedented precision. Social scientists use it to foster civil discourse. Historians uncover patterns across centuries of archives. Ethicists and legal scholars are shaping national conversations around fairness, governance and transparency. And artists are co-creating music, visual works and immersive design experiences with generative AI.
Yet no single research group can keep pace with AI’s evolution alone. Institutional effort is essential. That’s why U-M launched the nation’s first full suite of in-house generative AI tools, including U-M GPT, Go Blue virtual assistants, and Maizey, a secure platform built to support data-intensive research. These tools are already helping faculty and students work more efficiently, explore new questions, and bring bold ideas to life.
We’re also investing in people. MIDAS and partners across campus are developing creative ways to upskill the research workforce, from students to faculty. Programs such as the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship are preparing the next generation of AI-powered research leaders.
And we’re not stopping there. Campus-wide initiatives like AI Institutes at Michigan (AIIM) are building the next chapter of AI innovation, anchored in academic excellence and responsible deployment.
But these are just the first steps.
AI has been called a “general-purpose technology,” with the potential to become so embedded in daily life that users interact with it seamlessly, without technical expertise. If that’s the future we want for research, how do we get there? We’re only beginning to explore those questions. But one thing is clear: academic institutions must evolve as rapidly as the technologies they adopt. U-M is positioned to lead.
Still, we must reckon with the challenges that AI presents. AI amplifies values, for better or worse. It heightens creativity but can deepen bias. It curates what we see and how we think. It offers new ways to cut corners, risking scientific rigor. It can compound the damage of careless decisions when over-relied upon. That’s why many U-M researchers are working to ensure AI is used responsibly in science. MIDAS is now focusing its longstanding commitment to research rigor and reproducibility on building trust in AI-powered research.
Ultimately, our AI efforts must serve a higher purpose: the “why” behind research itself. Whether it’s solving urgent problems, advancing knowledge or, as physicist Richard Feynman put it, simply “the pleasure of finding things out,” our use of AI should reflect our intentions, not just its capabilities. In this regard, U-M’s ethicists, historians and philosophers offer critical insights that help guide technologists.
AI is reshaping how we do research, what discovery means and how institutions must evolve. These shifts will be profound. It is our duty to ensure they move society forward.