EXPRESS: Governing AI-Enabled Decision Making: Delegation, Autonomy, and Control at the Operations–Marketing Interface

Firms are increasingly confronting a fundamental organizational choice: whether to retain human control over operational and marketing decisions or to delegate decision authority to agentic artificial intelligence systems. While recent advances in generative and autonomous AI enable real-time pricing, inventory allocation, and demand coordination, firms exhibit substantial heterogeneity in how much autonomy they grant these systems—ranging from full automation to extensive human oversight. This raises a central operations management question: when should firms delegate pricing and inventory decisions to agentic AI, and how should such delegation be governed? We develop an analytical model of AI delegation at the operations–marketing interface in which a firm jointly determines pricing and inventory under demand uncertainty and chooses among human control, full AI autonomy, or human-in-the-loop governance. Agentic AI improves responsiveness by enabling state-contingent decisions, but also introduces new forms of operational exposure by reducing buffers and accelerating execution. Our analysis yields several key insights. First, we identify a demand-variance threshold above which delegating decisions to agentic AI becomes optimal, even when AI is imperfect. Second, we show that partial delegation can strictly dominate both full autonomy and full human control, providing a theoretical foundation for hybrid governance structures widely observed in practice. Third, when AI investment is endogenous, adoption and autonomy become distinct decisions, generating a three-region equilibrium in which firms may invest in AI while deliberately restricting its authority. We further show that learning, service-level asymmetry, stochastic lead time, endogenous human oversight, and organizational scale fundamentally reshape delegation incentives, often in counterintuitive ways: faster learning can delay early autonomy; improved pricing coordination can increase inventory imbalance; and larger organizations may rely on autonomy even under moderate uncertainty. Together, our results demonstrate that AI delegation is not a technological inevitability but an economically contingent organizational choice shaped by uncertainty, risk asymmetry, and structural complexity. The paper provides a unified theoretical framework for understanding AI governance in operations and offers guidance for firms navigating the transition toward autonomous decision-making.