Artificial intelligence (AI) has received significant attention in recent years, primarily due to breakthroughs in game playing, computer vision, and natural language processing that have captured the imagination of the scientific community and the public at large. Businesses, industries, government agencies, and academic disciplines around the world are now contemplating the application of AI to their own challenges. Many of them are part of the INFORMS ecosystem and are looking at how AI will transform their operations and how it relates to the methodologies and algorithms used in the operations research (O.R.) community.

This Editor’s Cut “Advances in Integrating AI & O.R.” emerged from the strategic initiative in artificial intelligence started in 2019 by then president Ramayya Krishnan to understand the impact of AI on the INFORMS community. The initiative focused on how the fusion of AI and O.R. would be greater than the sum of the parts. It recognized that machine learning has adopted, refined, and expanded optimization algorithms that originated in the O.R. community, including stochastic gradient descent and convex optimization, and that AI will benefit from advances in decision making under uncertainty in its quest to develop robust intelligence. It observed that AI may help the INFORMS community to build a new generation of optimization solvers and algorithms, and provide new data-driven methods that nicely complement the traditional model-based methodologies of O.R. The initiative also saw many areas where cross-fertilization is not only possible but desirable for addressing some of the challenges that our societies are facing.

The INFORMS strategic initiative in AI resulted in a white paper that summarized the findings and provided a number of recommendations for the INFORMS community. This volume of Editor’s Cut complements the white paper and assembles a collection of papers from the INFORMS community that bridge the AI and O.R. communities. The papers are grouped into five categories:

1. Blending Predictive and Prescriptive Methods
2. AI/ML for Optimization Problems
3. Integrating Predictive and Causal Inference
4. Games, Control, Data-intensive Preference Estimation
5. Unstructured Data Analytics, AI and OR/MS – Innovative Applications

We recognize that some of the papers transcend this classification. Further, this assembled collection of papers is obviously not meant to be exhaustive or comprehensive: it is already becoming impossible to do so. In fact, the scope of these categories compellingly demonstrates the breadth and depth of research and applications at the intersection of AI and O.R. They show how to integrate predictive and prescriptive methods and how AI can help some of the hardest optimization problems. The collection also shows how to integrate predictive and causal inference, a topic that is increasingly important in a data-driven world; it also highlights the wide variety of innovative applications at the core of the INFORMS community that are already benefiting from the complementarity between AI and O.R.

We hope that you enjoy reading these papers as much we did, and encourage you to dive into these fascinating developments in our field.