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Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier

Author

Listed:
  • Holly Wiberg

    (Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Tinglong Dai

    (Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202; and Data Science and AI Institute, Johns Hopkins University, Baltimore, Maryland 21218)

  • Henry Lam

    (Department of Industrial Engineering & Operations Research, Columbia University, New York, New York 10027)

  • Radhika Kulkarni

    (Independent Researcher)

Abstract

In 1987, Herbert Simon envisioned a partnership between artificial intelligence (AI) and operations research and management science (OR/MS) to improve decision making. Nearly four decades later, as AI advances at a breakneck pace, Simon’s vision remains relevant. This paper revisits Simon’s perspective through a 2024 survey of Fellows of the Institute for Operations Research and the Management Sciences, capturing reflections from leading scholars and practitioners on how AI and OR/MS intersect today. The survey results highlight both opportunities and challenges. Many respondents see AI as a powerful tool that can complement OR/MS’s structured approaches, such as in problem formulation and optimization. At the same time, they emphasize the importance of maintaining OR/MS’s core strengths and identity, including its emphasis on mathematical rigor and interpretability. Although AI has opened new frontiers, its integration into OR/MS continues to evolve, shaped by shifts in research priorities, funding patterns, and educational needs. This article takes stock of where the collaboration between AI and OR/MS stands today and considers its future trajectory. The findings suggest that the two fields have much to gain from deeper engagement but that thoughtful alignment will be key. We hope that these insights contribute to an ongoing dialogue about how AI and OR/MS can inform and strengthen each other in the years ahead.

Suggested Citation

  • Holly Wiberg & Tinglong Dai & Henry Lam & Radhika Kulkarni, 2026. "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier," INFORMS Joural on Data Science, INFORMS, vol. 5(1), pages 14-23, January.
    • Handle: RePEc:inm:orijds:v:5:y:2026:i:1:p:14-23
    DOI: 10.1287/ijds.2025.0077
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    References listed on IDEAS

    as
    1. Herbert A. Simon, 1987. "Two Heads Are Better than One: The Collaboration between AI and OR," Interfaces, INFORMS, vol. 17(4), pages 8-15, August.
    2. Michele Samorani & Shannon L. Harris & Linda Goler Blount & Haibing Lu & Michael A. Santoro, 2022. "Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling," Manufacturing & Service Operations Management, INFORMS, vol. 24(6), pages 2825-2842, November.
    3. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    4. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    5. Julian Senoner & Torbjørn Netland & Stefan Feuerriegel, 2022. "Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing," Management Science, INFORMS, vol. 68(8), pages 5704-5723, August.
    6. Stuart Dreyfus, 2002. "Richard Bellman on the Birth of Dynamic Programming," Operations Research, INFORMS, vol. 50(1), pages 48-51, February.
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