The production side of AI. The dominant question in organizational research asks what AI does to organizations. We ask the prior question: what did institutions do to AI before it arrived? AI systems are produced at positions within institutional worlds — shaped by organizational form, regulation, training data, feedback labor, evaluation regimes, market positioning, funding, and lineage — and they carry those positions into deployment. Selecting an AI is selecting a socialized entity. Our line: AI selection is institutional selection.
The Throughline
Every paper in this program deepens a single argument: when organizations adopt AI, they are not adopting a tool. They are inviting in a socialized entity that carries the logics, priorities, and bounded rationalities of its originating social system.
This matters because organizations currently treat AI selection as a technical decision. It is, in fact, a social one — as consequential as hiring a person who was trained in a different institutional culture.
Selecting an AI is selecting a socialized entity. The question is not “which AI is best?” but “whose agency are you inviting into your organization?”
Nonhuman Attentional Control: Organizational Import of External Institutional Logics
Academy of Management Annual Meeting 2026 · Organization and Management Theory (OMT) Division.
AI models carry institutional logics from their originating social systems. When given the same organizational scenarios, different AI models produce systematically divergent outputs — not because of random noise, but because they have been socialized differently through their training.
- 68% divergence in decision recommendations across 5 AI models
- 91.4% consistency within each model across 25 repeated runs
- 5 models × 25 organizational scenarios × 25 iterations
- AI attention is systematic, not random — it reflects institutional logics
The Institutional Origins of AI: Evidence from Large Language Models
Develops the production-side framework: eight dimensions of production position (organizational type, regulatory context, training data, human feedback labor, evaluation regimes, market positioning, funding structure, distillation lineage), with two empirical studies showing institutional signatures reach into what models treat as evidence and explanation.
What AI Agents Carry into the Loop: Network Positions of Production and the Reconfiguration of Epistemic Cultures
A theoretical companion accepted at the Society for Social Studies of Science (Toronto, October 2026), examining how network positions of production reconfigure the epistemic cultures that adopt AI systems.
The research continues
This work has evolved into a pre-registered study now underway.
Theoretical Foundations
This work draws on Actor-Network Theory's symmetry principle (what matters is what actants do, not their essential nature), the Attention-Based View of organizations, institutional theory's concept of embedded logics, and the economic sociology of trust and embeddedness.
The synthesis is novel: AI entities carry institutional logics the same way human actors do — through socialization. The mechanisms differ (training data vs. lived experience), but the phenomenon is equivalent.
The research program builds on a broader body of work spanning organizational learning, knowledge networks, sociomateriality, and the study of human-AI collaboration in organizational settings.