Survey of INFORMS Fellows Suggests Opportunities, Challenges at the Intersection of AI and Operations Research/Management Science
In 1987, Herbert Simon, a computer science pioneer and longtime faculty member at Carnegie Mellon University, envisioned a partnership between artificial intelligence (AI) and operations research/management science (OR/MS) to improve decision making. In a new study, researchers surveyed fellows of the Institute for Operations Research and Management Sciences (INFORMS) to revisit the collaboration between AI and OR/MS in today’s rapidly changing technology landscape.
Their findings have been synthesized in a new perspective article in the INFORMS Journal on Data Science, co-authored by Holly Wiberg (Carnegie Mellon University), Tinglong Dai (Johns Hopkins University), Henry Lam (Columbia University), and Radhika Kulkarni (an independent researcher). Their work was supported by the INFORMS AI Ad Hoc Roadmap Committee, as well as by Elena Gerstmann, Jeff Cohen, and Matt Walls from INFORMS.
“The Fellows survey gave us a unique opportunity to hear directly from leaders in OR/MS on how they view the evolution, current state, and future prospects of AI-OR/MS collaboration,” says Holly Wiberg, assistant professor of operations research and public policy at Carnegie Mellon’s Heinz College. “Herbert Simon laid out an early vision for this collaboration nearly 40 years ago. It was striking to see that, despite dramatic changes in the field over this time, many of the core themes have remained remarkably consistent.”
Researchers surveyed INFORMS fellows in September and October 2024. They received responses from 45 fellows (a 21% response rate), who represented a broad cross-section of professional affiliations; nearly 70% identified as members of academic institutions, and the remaining were engaged in industry or nonacademic roles. Although the survey does not claim to be statistically representative of the entire INFORMS community, which includes more than 12,000 members across various career stages, it offers insight from OR/MS experts who have witnessed the transformation of these fields.
Responses to the survey indicate a complex landscape, according to the authors. The fellows surveyed largely share Simon’s vision of integration for two fields whose historical divergence may have been more institutional than intellectual. Today’s OR/MS professionals see clear complementarity between the structured optimization approaches of OR/MS and the flexible, heuristic AI methods. This recognition manifests not just in theoretical appreciation but in practical integration, with most respondents reporting significant AI components in their work.
They find wide recognition of the potential value of AI in OR/MS, and vice versa, but also an awareness of key challenges, including AI overshadowing other adjacent disciplines. Yet fulfilling Simon’s vision demands more than simply integrating OR/MS with AI or vice versa, the authors say. The divergence between views on AI by academics and by practitioners highlights the need to close the gap between theory and practice. At the same time, funding shifts and recruitment challenges press OR/MS to maintain its identity while adapting.
The authors make several recommendations for moving forward, including fostering collaboration through professional societies via joint conferences, journals, and initiatives; promoting cross-training in AI and OR/MS through education; and expanding support for interdisciplinary research. Some of these actions are within the community’s control, but others depend on institutional and policy change.
“Carnegie Mellon is among the birthplaces of both AI and OR/MS,” notes study coauthor Tinglong Dai, an alumnus of Carnegie Mellon (Tepper PhD ’13), and professor of business at Johns Hopkins University’s Carey Business School and the Data Science and AI Institute. “Living alongside the pioneers of both fields, we have known for decades that the synergies between AI and OR/MS represent one of the greatest opportunities in business and society. It may well be one of our best-kept intellectual secrets.
“Herbert Simon made this clear nearly 40 years ago,” Dai continues. “Today, that vision has become something of a consensus, and our findings affirm it. They point to a bright future for the AI and OR/MS communities, one that the Carnegie Mellon community is well positioned to continue leading.”
Adds Ramayya Krishnan, professor of management science and information systems and the dean emeritus of Carnegie Mellon’s Heinz College, and the 2019 president of INFORMS who helped formulate its AI strategy, “Simon visualized management science and operations research as part of an effort to comprehend and enhance intelligence. His vision was influential in our formulation of the AI OR strategy at INFORMS, and as the paper notes, there is considerable opportunity in operationalizing the synergies between the fields to benefit society.”
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Summarized from an article in INFORMS Journal on Data Science, "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier," by Wiberg, H (Carnegie Mellon University), Dai, T (Johns Hopkins University), Lam, H (Columbia University), and Kulkarni, R (independent researcher). Copyright 2025 INFORMS. All rights reserved.
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