Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory

Paper highlights

Organization theory has borrowed from artificial intelligence for decades, often without treating AI as one of its intellectual foundations. Ideas such as problemistic search, heuristics, exploration, organizational scripts, requisite variety, and the wisdom of crowds all have roots in AI research.

The connection is structural. An organization and an AI system are both artificial arrangements built to produce intelligent behavior from limited components. Each must search for possible actions, represent a complicated environment, and aggregate partial information. That parallel makes AI more than a technology used by organizations; it is also a collection of models for understanding how organizations work.

What the review covers

The review traces more than 100 organization-theory studies to ten AI approaches, ranging from control theory and heuristic search to knowledge representation, neural networks, reinforcement learning, evolutionary computation, and ensembles. It organizes those borrowings around three cognitive tasks:

• Search: finding workable actions without evaluating every possibility.
• Representation: constructing a simplified model of the environment.
• Aggregation: combining information or judgments distributed across many components.

What follows from the analogy

Some organization theories still rely on versions of AI ideas that were current when the borrowing occurred, even though AI has since developed new methods. Neural networks may offer models of bottom-up information processing; distributed AI may illuminate coordination and negotiation; newer forms of representation may sharpen research on managerial mental models.

Why it matters

• Search, representation, and aggregation provide a common language for comparing organizations and AI systems without treating them as identical.
• Many ideas in organization theory grew from computational questions about limited search, encoded knowledge, and combining weak judgments.
• The exchange can run both ways: AI architectures suggest organizational mechanisms, while organization theory exposes what machine analogies omit about goals, authority, incentives, and coordination.

How to use this paper

Cite this for

• A review of more than 100 organization-theory studies that draw on AI ideas.
• Search, representation, and aggregation as a shared vocabulary for organizations and AI systems.
• The claim that AI is an intellectual source for organization theory, not only a technology organizations use.

Useful for teaching

• The AI roots of familiar organization-theory ideas such as problemistic search, heuristics, scripts, and crowds.
• How analogies can travel from computational systems to organizations without making them identical.
• Why newer AI architectures may reopen old organization-theory questions.

Careful claim

The review shows that AI has supplied models and analogies for organization theory; it does not imply that organizations are literally AI systems or that every AI method should be imported into organization research.

Abstract

A rarely acknowledged fact about organization theory (OT) is that many of its ideas stem from the field of artificial intelligence (AI). For example, key OT concepts such as problemistic search, heuristics, exploration, requisite variety, and organizational scripts, all have their roots in AI.

The main goal of this paper is to expose the full range of AI ideas that have been used in OT. We do so by explaining key AI ideas and showing how OT used them. Our review covers over 100 OT works that depend on AI ideas both critically and explicitly. We group these ideas into ten AI approaches that speak to three fundamental processes in organizations: search, representation, and aggregation.

We argue that this broad and deep borrowing from AI stems from fundamental structural similarities between AI and OT, as both fields study how artificial systems (programs and organizations) can pursue intelligent behavior. We also identify areas of AI from which OT scholars may continue to draw inspiration and suggest ways in which AI technologies may continue to affect organizations. Overall, our work shows that, beyond its effect as a technology, AI has given OT a set of models about how organizations work.

Other links

• SSRN preprint

Last updated 2026-06-21