Research

Abstract: A substantial body of research on AI examines its ability to perform tasks; far less its ability to coordinate activities across an organization. I study this role at one of the world's largest container ports, where a central AI dispatches autonomous vehicles operating alone (AI-dominant) or alongside human drivers (mixed human-AI). Across 179,925 tasks over 364 days, using quasi-exogenous mechanical faults as disruptions, I document a tradeoff: AI-dominant areas are more productive under normal conditions but worse at handling exceptions - localized failures spread further and cost more. A formal model traces both to one mechanism: unified AI coordination tightens coupling between activities, raising steady-state performance while amplifying losses from stochastic shocks. Because organizations are open systems, AI's gains on one performance dimension may come at the expense of another.

Abstract: Research on GenAI and work has concentrated at the task level. We ask when task-level effects alter knowledge-work industry architecture: the allocation of activities across firm boundaries. Using a three-stage abductive design—an executive roundtable, a panel of 626 firm websites (2017–2025), and 33 interviews plus two further roundtables with C-level executives and corporate directors—we document divergent repositioning: professional services firms foreground relational value, while interface-oriented technology firms emphasize scalable interfaces. We find that activity reallocation is shaped by two independently varying dimensions: expertise bypass and exchange governability. Their intersection yields four configurations: contestable reallocation, persistent bundles with selective insourcing, federated expert provision, and integrated expert provision. We extend industry architecture theory by explaining change when production and exchange conditions shift separately.

Abstract: How do senior decision-makers assess the competitive implications of GenAI? Using an abductive, multi-stage study of 531 UK corporate directors (exploratory roundtables, survey, matched stimulus roundtables, and follow-up interviews, 2024–2026), we document that directors distinguish three dimensions of GenAI’s competitive implications: competitive differentiation, firm displacement, and industry disruption. Expectations vary markedly even within the same sub-sector. This heterogeneity is patterned by how directors frame their firms’ value propositions—especially perceived modularity and reliance on pattern recognition—and by complements and buffers such as proprietary data, relationship-intensive delivery, and regulatory/liability constraints. Across waves, directors’ competitive expectations remain stable, consistent with durable interpretive frames during early diffusion.

Abstract: Across industries, AI is taking over tasks once performed by humans, yet humans are often kept “in the loop” when systems reach exceptions. We study human intervention at one of the world’s largest ports in China, where autonomous trucks can be remotely taken over when stuck. Exploiting a capacity constraint and matching on pre-treatment operational characteristics, we find that human takeover reduces task completion time by roughly 7%, with gains concentrated when exceptions are neither too simple nor too complex. However, humans do not add value in selecting which trucks to take over, partly due to an attention bias. Our results align with Garicano’s (2000) management-by-exception logic but point to an important extension: in dynamic AI systems, which exceptions should be handled by humans may not be immediately obvious, underscoring the importance of carefully designing human-in-the-loop rules.

Abstract: We examine how digital infrastructure transforms coordination mechanisms and synergy patterns within multi-business firms. Through a longitudinal study of a large retail conglomerate's evolution from traditional M-form to internal ecosystem, we document a three-stage process whereby digital capabilities progressively alter value creation dynamics. Our findings reveal how digital infrastructure enables systematic lateral coordination between previously unrelated businesses, replacing sporadic collaboration with data-driven discovery of synergistic opportunities. We identify novel forms of value creation—superadditive demand synergies and data-network effects—that differ from traditional resource-based relatedness. The corporate center's role evolves from orchestrating synergies to maintaining digital infrastructure while enabling autonomous collaboration. These findings explain how digitally enabled firms successfully pursue broader, seemingly unrelated scope through fundamentally different governance mechanisms than traditional conglomerates.