Knowledge that Transforms

A multiyear data transformation project at Caterpillar that has made the company AI-ready provides an exemplary case for what leadership commitment to such a technology project involves. CEOs must go beyond communicating abstract support, by setting a tangible, strategic business goal that the transformation will support; giving teams realistic time horizons and adequate resources; and assigning meaningful, instrumental roles to members of the leadership team.

New research on manufacturers moving to a value-based sales model finds that delivering initial solutions on a one-off basis is relatively straightforward. The challenge lies in scaling those solutions to more customers, which requires structured, repeatable processes and strong, entrenched capabilities. The researchers describe two important phases of capability building that they identified and define the organizational skills, processes, and relationships that successful companies assemble.

As AI systems take on more organizational decision-making, traditional models of accountability — focused on identifying a single culprit when something goes wrong — are breaking down. Drawing on recent research, the authors introduce narrative responsibility, a framework that maps the real story behind failures, distributes ownership across teams, and embeds ongoing reflection into everyday practice. This approach is essential for organizations navigating the complexity of AI-enabled decisions.

Digital platforms and generative AI have made it easier than ever for startups anywhere to access global talent and markets, but companies outside major hubs still struggle to scale. The culprit isn’t a lack of technology — it’s a lack of strategic focus. Research points to two traps companies fall into, but applying a practical framework can help them compete on their own terms, regardless of location.

Artificial intelligence (AI)-automated decision systems encounter persistent, interdependent, and dynamic fairness tensions that traditional one-off interventions cannot resolve. Because these tensions persist due to interdependence and dynamic interaction, organizations require both a theory of the problem to explain their persistence and a theory of the solution to prescribe how they can be managed. Our design theory, FAIR (Fairness Adaptation through AI-augmented Responsiveness), provides a theory of the problem by reframing AI fairness as a sociotechnical paradox constituted within AI artifacts that automate decision tasks, through interdependent organizational, technical, and governance choices and their interaction with regulatory mandates and societal norms. Synthesizing four fairness perspectives (Ethics, Organizational Justice, Economic Fairness, and Rawlsian Justice), we identify three metatheoretical dimensions (principles, goals, foci) and show that the interdependence within and among these dimensions is the root, endogenous source that constitutes paradoxical fairness tensions. Building on this diagnosis, FAIR provides a theory of the solution by specifying an organizational capability grounded in three design foundations. First, the paradox lens motivates iterative adaptive cycles (Surfacing and Resolving) to continually surface and resolve AI fairness tensions. Second, design science in information systems and computer science distinguishes AI artifacts (the “what”) from the actors (the “who”) responsible for adapting them, establishing the basis for complementary human–AI agent collaboration in the adaptive cycles: AI agents execute monitoring to surface and refinement to resolve tensions, whereas human agents specify objectives, adjudicate trade-offs, and exercise contextual judgment and oversight. Third, the managing-with-AI literature informs how this human–AI agent collaboration should be governed. These foundations yield two reinforcing mechanisms: (i) artifact-level adaptation, achieved through structured human–AI agent collaboration, within and across the layers of the AI decision pipeline—Representation (data), Learning (model), and Calibration (decision); and (ii) portfolio-level, risk-tiered federated governance that structures how human–AI agent collaboration scales across tasks and artifacts, balancing process standardization with configuration choices and human control with AI autonomy based on task risk. Enabled by organizational “fairness complements”—namely, human skills to work with AI agents and structured stakeholder feedback—this sociotechnical design provides organizations with a sustained capability to harmonize global coherence and local flexibility in the responsive adaptation of AI fairness.

This commentary explores multiple perspectives on the potential use of digital technologies to improve organizational resilience in the context of climate change. Such an approach is needed to address this complex problem space, especially since it encompasses a wide variety of phenomena, including floods and landslides, disruptions to global supply chains, heat waves, biodiversity loss, greenhouse gas emissions, and food insecurity. We assembled a diverse set of five scholarly teams specializing in multiple problem topics, research approaches, and theoretical perspectives on this project. Each team identified and problematized a specific facet of digital resilience for the climate crisis. The perspectives cover a range of rich narratives, including digital resilience in the context of floods and landslides in Brazil and Indonesia, conceptual development efforts incorporating the natural environment with people and technology, reconceptualization of the problem space in terms of time and type, and two applications of digital resilience in the domains of global supply chains and carbon emissions tracking. This research commentary thus presents a multi-perspective examination and interrogation of digital resilience for addressing the climate crisis, out of which four transcending themes emerge: the need to integrate nature into sociotechnical thinking, the need to examine actions at both micro and macro levels, the need to include both reactive and proactive strategies, and the need to view climate crisis as a process rather than a series of events. This commentary aims to motivate other scholars who take diverse theoretical perspectives to join us in developing fundamental knowledge and practical solutions needed to achieve digital resilience for the climate crisis.

The classic digital divide theory asserts that unequal access to and unequal experience with information technologies may lead to unequal user outcomes. This paper introduces a new perspective to extend this theory: outcome divides can persist despite equal access and equal experience if users differ in their analytical ability to analyze and interpret available data for decision-making. We term this new data-to-decision skill as analytical ability and integrate it into the classic digital divide framework. We develop a new approach to operationalize analytical ability by contrasting humans’ actual performance against that of a standard machine learning model that makes similar analytical decisions based on the same information available to humans, essentially emulating a quasi-random counterfactual setting. To minimize the confounding impact of other divides, we validate the role of analytical ability in information-transparent environments like the blockchain-based trading markets, where all historical trading data is equally available to all users on the blockchain. We leverage data from EnjinX, a blockchain-enabled non-fungible token (NFT) marketplace that records all historical NFT transactions. We measure user outcomes by their flip trading performance, a standard metric captured via the percentage of exploited flipping opportunities. Our empirical analysis reveals that disparities in analytical ability may become the new bottleneck for outcome equity: flip trading performance could decrease by 66.86% when traders are incapable of analyzing the available blockchain information effectively. Our study contributes to the literature by extending the digital divide theory with the notion of the analytical ability divide. Moreover, we are among the first to rigorously quantify analytical ability and empirically test its impact based on the extended digital divide framework. Our study also offers important practical implications for platforms and policymakers to bridge this new divide in order to foster outcome equity.

Problem Definition: Managing inpatient flow in large hospital systems is challenging due to the complexity of assigning randomly arriving patients -- either waiting for primary units or being overflowed to alternative units. Current practices rely on ad-hoc rules, while prior analytical approaches struggle with the intractably large state and action spaces inherent in patient-unit matching. A scalable decision-support framework is needed to optimize overflow management while accounting for time-periodic fluctuations in patient flow. Methodology/Results: We develop a scalable decision-making framework using Proximal Policy Optimization (PPO) to optimize overflow decisions in a time-periodic, long-run average cost setting. To address the combinatorial complexity, we introduce atomic actions, which decompose multi-patient routing into sequential, tractable assignments. We further enhance computational efficiency through a partially-shared policy network designed to balance parameter sharing with time-specific policy adaptations, and a queueing-informed value function approximation to improve policy evaluation. Our method significantly reduces the need for extensive simulation data, a common limitation in reinforcement learning applications. Case studies on hospital systems with up to twenty patient classes and twenty wards demonstrate that our approach matches or outperforms existing benchmarks, including approximate dynamic programming, which is computationally infeasible beyond five wards. Managerial Implications: Our framework offers a scalable, efficient, and explainable solution for managing patient flow in complex hospital systems. More broadly, our results highlight that domain-aware adaptation -- leveraging queueing structures and operational insights -- is more critical to improving algorithm performance than fine-tuning neural network parameters when applying general-purpose algorithms to specific domain applications.

Problem definition: During pandemics, policymakers must make critical decisions about public health interventions and allocations of scarce resources in response to rapidly evolving diseases under high levels of uncertainty. Epidemiological models, such as the Susceptible-Exposed-Infectious-Recovered-type (SEIR-type) compartmental model, are indispensable tools for predicting how a pandemic may spread over time and how different public health interventions could affect the outcome. Based on such predictions, deterministic compartmental optimization models can be adopted to attain effective public health intervention decisions. However, deterministic models often neglect parameter uncertainty and the risks inherent in the stochastic compartment dynamics, leading to less robust solutions. Methodology/results: To address these issues, we develop an epidemiological analytics framework based on the ambiguity tolerance measure and stochastic compartmental models. We introduce a robust epidemiological optimization model that lexicographically minimizes the ambiguity tolerances associated with violating healthcare resource constraints. Leveraging the asymptotic Gaussian property, we employ Gaussian approximation to enhance the efficiency of evaluating robust epidemiological constraints. To streamline and automate its application for practitioners and policymakers, we develop a Python-based robust epidemiological analytics modeling (REALM) toolkit. Managerial implications: Employing real-world data from Singapore, we investigate various resource management scenarios, including testing, bed, and vaccine capacity allocations. Our numerical results showcase that our robust epidemiological analytics models outperform deterministic counterpart benchmarks, particularly in the number of hospitalized cases and deaths, given healthcare resource capacity constraints. The results demonstrate the benefits of accounting for risk and ambiguity in disease propagation when addressing epidemiological optimization models. Funding: The research of C. Fu was supported by the National Natural Science Foundation of China [Grants 72401229, 72310107003, and 72271201]. The research of M. Zhou was supported by the National Natural Science Foundation of China [Grants 72301075 and 72293564/72293560]. The research of J. Xie was supported by the Deutsche Forschungsgemeinschaft [Grant 543063591]. The research of M. Sim was supported by the Ministry of Education, Singapore under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0984 .

Problem definition: A key choice in operational decision making is whether to collocate services. Although prior work has highlighted the benefits of collocation, these benefits may need to be balanced with potential costs. Thus, it is critical to understand not just whether collocation matters, but also when and for whom. We consider collocation in the context of healthcare and ask: Does collocation of mental and physical health resources improve outcomes? This is important, as primary care serves as a gateway to address mental health concerns. We next study collocation’s relationship with patient complexity and with three social risk factors: age, race, and income. Finally, we investigate two pathways through which collocation impacts outcomes. Methodology/results: As America’s largest integrated healthcare system, the Veterans Health Administration offers an excellent setting to investigate these questions. We empirically analyze more than 112,000 patients—over an 11-year period—who suffer from chronic conditions and show evidence of mental illness. We find that collocation is associated with improvement in four key outcomes: hospitalizations, length of stay (LOS), 30-day readmissions, and suicidal behavior. For example, a one-standard-deviation increase in collocation is related to a 3.4% average reduction in LOS, roughly equivalent to a savings of $3.6 million annually, just for our cohort, with the majority of the savings coming from severely ill patients. Further, collocation benefits patients who are younger, are non-Hispanic Blacks, and those with low incomes. Finally, our analysis reveals that collocation improves outcomes (partially) through a reduction in no-shows and an increase in medication adherence. Managerial implications: Our work demonstrates the importance of collocation as a strategic operational lever and offers insights into where to target collocation and, broadly, how to design an operationally efficient system. Theoretically, we advance the location literature, emphasize task complexity as a key moderator, and highlight collocation’s value in addressing health/social inequities. Funding: C. A. Alvarez received research support from the National Center for Advancing Translational Sciences of the National Institutes of Health [Award UL1 TR003163]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0662 .