From Automation to Agency: Distributed Algorithmic Authority in Physical AI-Enabled Warehouse Execution Systems
TL;DR
Physical AI should be understood not simply as an automation capability, but as a socio-technical reconfiguration of execution and control whose effects depend on whether distributed operational authority remains legible, revocable, and governable at the system level.
Abstract
(1) Background: Physical artificial intelligence is progressing beyond performing tasks. As embodied AI systems integrate sensing, context-sensitive inference, orchestration, and physical actuation, the central issue shifts from automation efficiency to the redistribution of decision authority within organizational execution. (2) Methods: This paper develops a conceptual systems framework through structured, problem-centered conceptual theorizing. It integrates organizational information processing theory, requisite-variety and viable-system reasoning, near-decomposability and interactive-complexity perspectives, human-factors research, organizational authority, and socio-technical systems theory. (3) Results: The framework conceptualizes physical AI as a facility-level socio-technical execution architecture and introduces distributed algorithmic authority (DAA) as the degree to which real operational decision authority is distributed across interacting human, algorithmic, and embodied components. DAA comprises four dimensions: the scope of delegated decision rights, the locus and distribution of authority, revocability and intervention rights, and decision legibility. The framework distinguishes DAA intensity from DAA controllability and proposes an inverted-U relationship between DAA intensity and facility-level system performance. Greater DAA intensity can improve local responsiveness while eventually reducing coordination coherence and corrective control when the variety of distributed decisions exceeds the architecture’s coordinating capacity. DAA controllability and system-level control capacity—comprising governance maturity, coordinating variety, intervention capacity, and data integrity—shift the viable threshold of distributed authority. (4) Conclusions: Physical AI should be understood not simply as an automation capability, but as a socio-technical reconfiguration of execution and control whose effects depend on whether distributed operational authority remains legible, revocable, and governable at the system level.