To Irv Lustig:
Irv, please see below my response to this discussion that I posted about 10 days ago. I completely agree with your comments to Radhika. I am amazed how similar are your comments to my response below. I had a chance to exchange these issues with Holly Wiberg. She is a coauthor of the article published in IJDS on Synergizing AI and OR/MS. Rahul Saxena also posted his reply on this article that is similar in conclusion to mine.
This is what I posted on INFORMS Open Forum Discussion:
"Regarding the ongoing discussion on synergizing AI and OR/MS, I am somewhat confused about "current AI rage". Any discussion can be meaningful only if the terms being discussed are clearly defined and have the same meaning for everybody involved in the discussion.
So, Operations Research (OR)/Management Science (MS) can broadly be defined as a methodology for developing managerial decisions for efficient allocating of material, human and financial resources needed for making the best possible decisions within given constraints using various mathematical and computer simulation methods. The main OR/MS tools are linear/non-linear deterministic or stochastic optimization, sequential decision-making in random environment, discrete-event, system dynamic or Monte-Carlo simulation, and some other mathematical techniques. This is a rather developed area with numerous examples of successful and practically relevant applications.
Now, what is artificial intelligence (AI)? Despite a lot of AI talking and AI rage, I have not seen yet a satisfactory definition that would help everybody be on the same page discussing the same thing. Indeed, one general and strong definition is that AI is the simulation of human intelligence by computers. However, it is not clear how to define human intelligence in the first place.
On the other hand, there is a weak AI definition: it is the narrow use of available AI technology, like machine learning or deep learning, to perform very specific tasks, such as playing chess, recommending songs/purchases based on past history, or steering cars. Also known as Artificial Narrow Intelligence (ANI), weak AI is essentially the kind of AI we use today. It can be, say, Generative AI that is a kind of artificial intelligence capable of producing original content, such as written text or images, in response to user inputs or "prompts." Generative models are also known as large language models (LLMs) because they're essentially complex, deep learning models trained on vast amounts of data that can be interacted with using normal human language rather than technical jargon.
So, how can OR/MS and AI defined above be synergized? And why such a synergy is needed at all? To enhance each other? In what way?
After all, OR/MS is clearly defined and is based on a solid practically tested methodology. The AI (any version) is fuzzy and is focused on some narrow specific tasks with rather limited business outcomes and business needs. AI can help in performing some tasks, but, to me, it is mostly a nice toy rather than necessary methodology to make a business difference.
Thank you for the discussion."
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Alexander Kolker
ge healthcare
MILWAUKEE WI
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Original Message:
Sent: 07-22-2025 18:11
From: Irv Lustig
Subject: Excited to share an upcoming article in IJDS on Synergizing Artificial Intelligence and Operations Research
Radhika:
Thanks for sharing the article and for the analysis of the responses from the Fellows. If I recall, I did participate in the survey, but there was one thing that concerned me, which is the definition of "AI" used in the survey. So I think that respondents may have different responses based on their own definition of AI.
Today, many people say "AI" and they really are referring to "Generative AI".
In the recent past, some people would say "AI" and they were referring to "machine learning".
My friend and past colleague Mike Watson gave a keynote talk at the 2020 INFORMS Virtual Annual Meeting described here: https://pubsonline.informs.org/do/10.1287/orms.2020.05.44n/full/ where Mike argued that the types of work we do as OR/MS professionals can fall under the AI umbrella. More recently, Mike describes 4 different ways that people interpret the meaning of "AI". See https://miketalksai.substack.com/p/what-ceos-leaders-and-investors-need
So I think it is important that whenever we, in the INFORMS community, use the phrase "Artificial Intelligence" or its abbreviation "AI", we define what we mean when we use those terms. Simon's article in 1987 and your article in 2025 are probably using different definitions, and I'm pretty certain that the Fellows who responded to the survey used a variety of definitions when answering the questions.
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-Irv Lustig
Optimization Principal
Princeton Consultants
Original Message:
Sent: 07-09-2025 09:38
From: Radhika Kulkarni
Subject: Excited to share an upcoming article in IJDS on Synergizing Artificial Intelligence and Operations Research
Dear All,
I am excited to share our upcoming article in the INFORMS Journal on Data Science, co-authored with Tinglong Dai, Henry Lam, and Holly Wiberg, "Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier."
I hope this article spurs more discussion among our INFORMS members on the importance of increasing collaboration between Operations Research and Computer Science researchers and practitioners. I welcome comments from others to help us energize the collaboration initiative between OR and AI that has been an important one for INFORMS for the past several years and has been nurtured through multiple workshops, INFORMS ad hoc committees since 2019 and many other initiatives in support of this effort.
In this article we revisit Herbert Simon's seminal 1987 article, "Two Heads Are Better than One: The Collaboration Between AI and OR," and present new insights from a 2024 survey of INFORMS Fellows. The historical view in Simon's article and the current perspectives from the survey indicate strong support for cross-field collaboration, alongside persistent barriers. We outline opportunities to bridge these gaps, drawing on the Fellows' responses and ongoing initiatives in our community. It is interesting to note that Simon's message from nearly 40 years ago is still as relevant as ever. In fact, with the rapid advances in AI in almost every sphere of academic research, it is increasingly important to recognize the complementary strengths of the OR/MS and AI communities and the potential for significant societal impact through their synergy.
Our article is available here: https://lnkd.in/eCmZij5F. And, Simon's original 1987 article is at: https://lnkd.in/e8Qqgex7
This paper is part of a broader effort by the INFORMS AI Roadmap Ad Hoc Committee (AIR-COM) to drive forward AI-OR/MS collaboration. I am grateful to the members of this committee as well as the earlier AI committees for their support through the years. Special thanks to Elena Gerstmann, PhD, FASAE, CAE, Jeffrey M. Cohen, MBA, Matt Walls, and the broader INFORMS team for supporting the committee's work and administering the survey. Also, many thanks to Yu Ding and the IJDS editorial team for their support on this publication.
Most importantly, we thank the INFORMS Fellows who generously participated in both the pilot and main survey phases. Their insights and experience made this study possible.
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Dr. Radhika Kulkarni
VP, Advanced Analytics R&D (retired), SAS Institute Inc.
2022 INFORMS President
Durham NC
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