Academic articles
Practitioner articles
Working papers
Books
Book chapters
Case studies
Other publications
Subject(s)
Diversity and inclusion; Human resources management/organizational behavior
Keyword(s)
gender, backlash, negotiation, bargaining, gender gap
Volume
155
Journal Pages
819–838
ISSN (Online)
1939-2222
ISSN (Print)
0096-3445
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management
Keyword(s)
explainable AI, human-machine collaboration, overreliance, underreliance, cognitive effort, rational inattention
© 2026, INFORMS
ISSN (Online)
1526-5501
ISSN (Print)
0025–1909
Subject(s)
Management sciences, decision sciences and quantitative methods
Keyword(s)
learning and earning, dynamic pricing, advertising, inventory management
ISSN (Online)
1526–5498
ISSN (Print)
1523-4614
Subject(s)
Management sciences, decision sciences and quantitative methods
Subject(s)
Human resources management/organizational behavior
Keyword(s)
executive education, change management, experiential exercise
ISSN (Online)
2379-2981
Subject(s)
Technology, R&D management
Keyword(s)
paradox, dysfunctional dynamics, vicious cycle, goals, longitudinal qualitative, process model
Subject(s)
Economics, politics and business environment; Ethics and social responsibility
Keyword(s)
exploitation, vignettes, fairness, power, distribution
ISSN (Online)
1540-5907
ISSN (Print)
0092-5853
Subject(s)
Human resources management/organizational behavior; Strategy and general management
Keyword(s)
Social networks, network ties, organizational research
ISSN (Online)
2631-7877
ISSN (Print)
2631-7877
Subject(s)
Strategy and general management; Technology, R&D management
Keyword(s)
alliance termination; disintegration, innovation strategy, open innovation closure, relationship dissolution, tie dissolution
ISSN (Online)
2688-2639
ISSN (Print)
2688-2612
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
machine-learning, rational inattention, human-machine collaboration, cognitive effort
JEL Code(s)
L23, M11, O33, O31, D24
Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains, but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management—intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decisionmaking, risk management, and human–machine collaboration, and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.
© 2026, INFORMS
Volume
28
Journal Pages
iv–xix, 687–1009, iii
ISSN (Online)
1526–5498
ISSN (Print)
1523-4614