AI Agents in Knowledge-Intensive Organizations: Transforming Decision Processes, Coordination, and Innovation

David J. Teece

AI Agents in Knowledge-Intensive Organizations: Transforming Decision Processes, Coordination, and Innovation

Author(s)	David J. Teece
Country	United States
Abstract	Artificial intelligence agents are developing from passive analytical tools into semi-autonomous organizational participants capable of retrieving knowledge, decomposing goals, coordinating tasks, generating alternatives, and supporting decisions. This transformation is particularly consequential for knowledge-intensive organizations, where performance depends on the timely interpretation and integration of distributed expertise. The present simulation-based study examines how AI-agent integration may influence decision-process efficiency, interfunctional coordination, and innovation capability. A conceptual model was developed around four organizational conditions: AI-agent integration, knowledge accessibility, human oversight, and workflow interoperability. A simulated dataset representing 240 organizational units was generated to evaluate the model without presenting synthetic observations as evidence collected from real organizations. Descriptive analysis, correlation testing, hierarchical regression, and conditional-effect modeling were applied. The simulated results indicated that stronger AI-agent integration was associated with shorter decision cycles, higher coordination quality, and greater innovation output. However, the benefits weakened when organizational knowledge was fragmented, agent recommendations were insufficiently explainable, or responsibility for final decisions was unclear. Human oversight improved the relationship between AI-agent use and decision quality, whereas excessive dependence on automated recommendations increased the modeled risk of automation bias and knowledge homogenization. The study proposes that AI agents create organizational value when they are treated as governed participants within a sociotechnical system rather than as independent substitutes for professional judgment. Effective deployment therefore requires traceable knowledge sources, explicit decision rights, interoperable information systems, escalation mechanisms, and continuing human review. The article contributes an integrated framework connecting agentic capabilities with decision architecture, coordination routines, and innovation processes in knowledge-intensive settings.
Keywords	AI agents; knowledge-intensive organizations; human–AI collaboration; organizational decision-making; knowledge coordination; innovation capability; responsible artificial intelligence; simulation-based research.
Field	Engineering
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-05

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI Agents in Knowledge-Intensive Organizations: Transforming Decision Processes, Coordination, and Innovation

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