Hello everyone
I am writing today for two reasons. First and very importantly to wish you all happy holidays! But I am also writing to announce some exciting news for the M&SOM Journal and our community.
AI has been rapidly evolving and is changing the field of Operations. Its impact is touching numerous Operations applications that are ranging from healthcare to supply chains and more. One can argue the applications of AI arise and can arise in practically every area.
For this reason, starting this January 2026 and onwards, the M&SOM journal is establishing a new department called "AI in Operations".
The department editors will be Maxime Cohen (from McGill University) and Tinglong Dai (from Johns Hopkins University. I want to thank them both for agreeing to take on this role!
I am very excited about this new department and how it can help our community showcase and be an outlet for all the important work our community does in this area. Given the fast pace AI is evolving, we aim as a result to experiment with an expedited review process for timely and high impact papers.
I would like to welcome the two department editors to the editorial board of the journal and express my excitement on will come!
Below you can also find the department vision statement with more details.
Happy holidays everyone!
Cheers
Georgia (Perakis)
New Department: AI in Operations
Department Editors: Maxime C. Cohen, McGill University; and Tinglong Dai, Johns Hopkins University
AI is rapidly changing the business world as we know it, with recent advances in generative and agentic AI in particular reshaping how operational predictions and decisions are formulated, executed, monitored, and improved. These systems can synthesize information, analyze multimodal data, enrich and augment data, generate solutions, test what-if scenarios and counterfactuals, interact with humans through natural language, and-when integrated with systems and tools-plan and take action in complex workflows. Accordingly, the Department seeks rigorous and impactful research that explains when, how, and why these technologies create operational value (or fail to do so), and that advances the science and practice of designing, implementing, scaling, and governing AI-enabled operational systems.
Our understanding of AI in Operations-both in methods and applications-is broad, with an emphasis on work where generative and agentic AI meaningfully reshape decision processes, organizational design, and operational performance. We welcome papers that develop new theoretical frameworks, empirical evidence, behavioral foundations, and computational approaches, including but not limited to research on: (i) AI-enabled planning and control that integrates learning with optimization and simulation; (ii) "language-to-decision" systems that democratize advanced analytics and decision tools for practitioners; (iii) AI lifecycle operations (data pipelines, labeling, training, deployment, monitoring, retraining, and incident response), multi-agent AI systems, and the resource and governance constraints that shape them; (iv) human-AI collaboration in operational settings, including trust calibration, fairness and bias, override policies, accountability, incentives, and workforce adaptation; and (v) evaluation, transparency, and best practices that improve reliability and reproducibility, especially for agentic systems that act through tools. We also welcome papers that rigorously examine the workforce and societal implications of adopting and deploying modern AI systems in operational contexts. The Department embraces a variety of methods, including analytical modeling, optimization, stochastic models, econometrics and causal inference, field and lab experiments, and computational/algorithmic work. Given AI's cross-cutting nature, we are open to interdisciplinary contributions and papers that bridge traditional OM domains and emerging AI-enabled operational contexts.
Given the pace at which AI is evolving, we aim to experiment with an expedited review process for timely, high-impact papers, providing constructive feedback that shortens the review cycle and maximizes impact. To this end, we plan to explore new, opt-in approaches to the editorial process, with the goal of making reviews faster, fairer, more consistent, more transparent, and more constructive.
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Georgia Perakis
William F. Pounds Professor, MIT
Co-Director of the Operations Research Center
EIC M&SOM Journal
Visiting Scholar Harvard Business School
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