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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Theorizing Artificial Intelligence as an Organizational Actor: Insights
from a Narrative Review
Zulkiffly Baharom
Tunku Puteri Intan Safinaz School of Accountancy (TISSA-UUM), College of Business, Universiti Utara
Malaysia, Malaysia
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500144
Received: 13 May 2026; Accepted: 18 May 2026; Published: 09 June 2026
ABSTRACT
This narrative review examines how artificial intelligence (AI) is being theorized as an organizational actor
within management and organization studies. The paper synthesizes fragmented theoretical perspectives to
develop an integrated conceptual framework that identifies the antecedents, mechanisms, and contextual
conditions shaping AI organizational actorhood. A systematic search of Web of Science was conducted, yielding
47 peer-reviewed articles published between 2020 and 2026. The selected literature spans management, business,
and accounting disciplines. Through thematic analysis guided by institutional theory, agency theory, and
sociomateriality, the review critically synthesizes scholars' conceptualizations of AI's organizational actorhood
and proposes a testable conceptual framework. Four key themes emerge: (1) AI as an institutional actor subject
to and generative of institutional pressures; (2) AI as an economic agent with principal-agent dynamics; (3) AI
as a socio-material ensemble co-constituting organizational realities; and (4) AI's evolving autonomy from tool
to quasi-autonomous actor. The review reveals that, while AI is increasingly theorized to possess agentic
qualities, conceptualizations remain fragmented across theoretical silos. This review contributes a multi-
dimensional framework for theorizing AI organizational actorhood that integrates institutional, economic, and
socio-material perspectives. It identifies five antecedents, three moderators, three mediators, and four control
variables that collectively shape the emergence of AI as an organizational actor. For managers and policymakers,
recognizing AI as an organizational actor with emergent agency necessitates rethinking governance mechanisms,
accountability structures, and legitimacy strategies. This is the first narrative review to propose a comprehensive,
testable conceptual framework for AI organizational actorhood, synthesizing diverse theoretical traditions to
advance a coherent research agenda.
Keywords: Artificial intelligence, organizational actorhood, agency theory, institutional theory, narrative
review
INTRODUCTION
Background
AI has transitioned from a back-office automation tool to a visible, consequential presence in organizational life.
Contemporary organizations deploy AI not merely as passive technology but as systems that make decisions,
learn from outcomes, interact with humans, and increasingly shape organizational strategies and structures (Van
Rijmenam & Logue, 2021). The proliferation of generative AI, algorithmic management systems, and
autonomous agents has rendered obsolete the traditional view of technology as exogenous to organizations
(Phillips, 2026).
This technological transformation coincides with growing scholarly recognition that existing organizational
theories, developed in an era of human-centric organizing, may be ill-equipped to conceptualize AI's role. As
Leavitt et al. (2021) observe, "rapid advancements in machine learning" present "epistemological opportunities...
for promoting organizational theory." Yet the theoretical integration of AI into organizational studies remains
incipient and fragmented.
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Statement of the Problem
Despite accelerating AI adoption across industries, organizational theory lacks a coherent framework for
understanding AI as an organizational actor. Three interrelated problems motivate this review. First, theoretical
treatments of AI are scattered across disparate traditions, institutional theory, agency theory, sociomateriality,
and practice theory, with limited cross-fertilization (Dubey et al., 2024). Second, much research treats AI as an
external variable or tool rather than as an agentic entity, potentially overlooking the ways in which AI actively
constitutes organizational processes (Humberd & Latham, 2026). Third, the rapid evolution of AI capabilities,
from rule-based systems to generative and agentic AI, outpaces theoretical development, creating a widening
gap between organizational practice and scholarly understanding (Phillips, 2026).
The central question guiding this review is: How is artificial intelligence theorized as an organizational actor
within contemporary management and organization studies, and what antecedent, mediating, and moderating
conditions shape its emergence as such?
Objectives
This narrative review aims to:
1. Synthesize extant theoretical perspectives on AI's role, agency, and actorhood in organizations
2. Identify the key antecedents that drive AI's emergence as an organizational actor
3. Uncover the mediating mechanisms through which these antecedents exert their effects
4. Examine the contextual conditions that moderate these relationships
5. Develop an integrated, testable conceptual framework for future empirical research
Scope and Delimitation
The review focuses on peer-reviewed journal articles in management, business, and accounting disciplines
published between 2020 and 2026. This timeframe captures scholarship responding to recent advances in
generative and agentic AI while building on foundational work. The review is confined to theoretical and
conceptual articles, as well as empirical studies with significant theoretical contributions, that explicitly or
implicitly address AI's organizational role.
REVIEW OF RELATED LITERATURE
Theoretical Foundations of Organizational Actorhood
The concept of organizational actorhood has deep roots in organization theory. Traditional perspectives
conceptualize actors as human individuals or legally constituted entities (firms, governments) capable of
intentional action, bearing rights and responsibilities. Institutional theory, particularly its sociological variant,
extends actorhood to collective entities that possess legitimacy, exhibit agency, and respond to institutional
pressures (DiMaggio & Powell, 1983). However, this tradition has remained largely anthropocentric, assuming
actors possess consciousness, intentionality, and reflexivity.
Agency theory offers another foundational perspective, conceptualizing the principal-agent relationship as
fundamental to organizational governance (Jensen & Meckling, 1976). The agent, typically a human manager,
acts on behalf of the principal, creating dynamics of information asymmetry, moral hazard, and incentive
alignment. This framework presupposes an agent capable of exercising discretion and responding to incentives.
Sociomateriality challenges the separation of social and material realms, arguing that organizing emerges
through the constitutive entanglement of humans and technologies (Orlikowski, 2007). From this perspective,
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agency is not a property of discrete entities but an effect of socio-material assemblages. This tradition provides
conceptual resources for theorizing non-human agency while avoiding technological determinism.
Artificial Intelligence and Organization Theory
The intersection of AI and organization theory has generated growing scholarly attention. Rudko et al. (2025)
introduce new institutionalism as a framework for understanding AI's organizational significance, arguing that
AI is simultaneously "the product of institutional forces and... an institutional force in its own right." This dual
character positions AI as both constituted by and constitutive of organizational fields.
Van Rijmenam and Logue (2021) directly confront questions of AI agency and actorhood, arguing that "AI
agency and the rise of the artificially intelligent agent are both fundamentally different and yet increasingly
similar to human agency in terms of intentionality and reflexivity." They contend that as "Child AI" AI created
by other AI, emerges, human design becomes increasingly distant, amplifying AI's autonomous qualities.
Humberd and Latham (2026) draw parallels between AI's integration into firms' decision-making and the
emergence of the professional manager that prompted the birth of agency theory. They theorize "a point at which
the AI system will achieve a level of autonomy and self-determination to be considered an agent of the firm,"
necessitating reconsideration of agency mechanisms for human-AI alignment.
Theoretical Fragmentation and Integration Challenges
Despite these contributions, the literature exhibits significant fragmentation. Different theoretical traditions
emphasize different aspects of AI's organizational role:
Institutional theory focuses on legitimacy, isomorphism, and institutional pressures shaping AI adoption
(Rudko et al., 2025; Reis & Pinheiro, 2025)
Agency theory addresses principal-agent dynamics, control, and alignment between AI agents and principals
(Humberd & Latham, 2026)
Resource-based view examines AI as a capability and source of competitive advantage (Sposato & Dittmar,
2026; Chatterjee et al., 2021)
Sociomateriality explores the entanglement of humans and AI in practice (Van Rijmenam & Logue, 2021)
This fragmentation, while reflecting theoretical pluralism, impedes cumulative theoretical development. As
Phillips (2026) argues, mainstream organization theory scholars have "failed to include intelligent technologies
in their theorizing," rendering the field's core "increasingly irrelevant" to contemporary organizing.
THEORITICAL FRAMEWORK
This review adopts a multi-paradigm theoretical framework that integrates three complementary perspectives on
AI organizational actorhood:
Institutional Theory provides tools for understanding how AI is shaped by and shapes institutional environments.
Key concepts include institutional pressures (coercive, normative, mimetic) that drive AI adoption (Rana et al.,
2024), legitimacy as both constraint and resource for AI systems (Schilke & Reimann, 2025), and institutional
logics that guide sensemaking about AI (Fang et al., 2023).
Agency Theory illuminates the governance challenges posed by AI agents. Building on Humberd and Latham's
(2026) extension of agency theory to AI, this perspective addresses monitoring, incentive alignment, and the
fundamental challenge of aligning AI agents' actions with principals' interests.
Sociomateriality offers ontological resources for conceptualizing AI agency as emergent from human-
technology assemblages. This perspective rejects both technological determinism and social constructivism,
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instead theorizing agency as distributed across networks of humans and technologies (Van Rijmenam & Logue,
2021).
Proposed Conceptual Framework
Synthesizing the reviewed literature, we propose a comprehensive conceptual framework that identifies the
antecedents, mediators, moderators, and outcomes shaping AI organizational actorhood.
Conceptual Framework Diagram
Figure 1: Conceptual framework of the AI organizational framework.
Summary of Variables
Table 1: Summary of Variables in the Proposed Conceptual Framework
Variable Type
Variable Name
Definition/Measurement
Supporting
Literature
Dependent
Variable
AI Organizational
Actorhood
The extent to which AI is recognized as
possessing agentic qualities within
organizational contexts, measured
through: (a) decision authority
delegated to AI; (b) discretionary scope
of AI actions; (c) perceived legitimacy
of AI as an organizational participant;
(d) organizational embeddedness of AI
systems; (e) autonomy level achieved
Humberd &
Latham (2026);
Phillips (2026);
Van Rijmenam &
Logue (2021);
Leavitt et al. (2021)
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Variable Type
Variable Name
Definition/Measurement
Supporting
Literature
Independent
Variables
IV1: Institutional
Pressures
External forces driving AI adoption and
shaping its organizational role:
Coercive (regulatory requirements,
compliance mandates); Normative
(professional standards, industry
norms); Mimetic (competitive
imitation, peer adoption)
Rudko et al. (2025);
Reis & Pinheiro
(2025); Zhang et al.
(2025); Rana et al.
(2024); Bag et al.
(2021)
IV2:
Technological
Capabilities
Organizational resources enabling AI
deployment: AI infrastructure
(hardware, software platforms); Data
resources (volume, quality,
accessibility); Technical skills (AI
expertise, data science capabilities)
Sposato & Dittmar
(2026); Omrani et
al. (2024); Islam et
al. (2026); Bag et
al. (2021)
IV3: Strategic
Orientation
Organizational commitment to AI as
strategic priority: Innovation focus
(R&D intensity, technology adoption);
Digital strategy (formal AI roadmap);
AI vision (leadership commitment,
resource allocation)
Sarfraz et al.
(2026); Cimino et
al. (2026); Chen &
Dong (2025);
Chatterjee et al.
(2021)
IV4: Ethical
Framework
Principles governing AI design and
deployment: Fairness (absence of bias);
Accountability (clear responsibility
structures); Transparency
(explainability, disclosure); Accuracy
(reliability, validity); Autonomy
(human control preservation)
Das et al. (2026);
Alibasic (2025);
Rana et al. (2024);
Du & Xie (2021)
IV5: AI
Autonomy Level
Developmental stage of AI capabilities:
Tool AI (rule-based, human-
dependent); Assistant AI
(recommendation systems with human
oversight); Agentic AI (autonomous
decision-making, self-learning)
Humberd &
Latham (2026);
Islam et al. (2026);
Zhang et al. (2025);
Vvan Rijmenam &
Logue (2021)
Mediating
Variables
MED1:
Legitimacy
Social acceptance and perceived
appropriateness of AI: Pragmatic
legitimacy (utility-based); Moral
legitimacy (normative approval);
Cognitive legitimacy (taken-for-
grantedness)
Chakraborty &
Kumar (2026);
Schilke & Reimann
(2025); Rudko et al.
(2025); Liyanage et
al. (2025)
MED2: Trust
Willingness to rely on AI systems:
Ability trust (competence beliefs);
Chakraborty &
Kumar (2026);
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Variable Type
Variable Name
Definition/Measurement
Supporting
Literature
Benevolence trust (perceived goodwill);
Integrity trust (adherence to principles)
Schilke & Reimann
(2025); Fang et al.
(2023)
MED3: Decision
Rights
Allocation of authority between humans
and AI: Formal authority delegation;
De facto decision autonomy; Human
oversight mechanisms; Escalation
protocols
Humberd &
Latham (2026);
Bechky & Davis
(2025); Leavitt et
al. (2021)
Moderating
Variables
MOD1:
Governance
Mechanisms
Organizational structures overseeing
AI: Board-level AI committees; Ethical
review boards; Algorithmic
accountability offices; Monitoring and
audit systems
Humberd &
Latham (2026);
Chakraborty &
Kumar (2026);
Alibasic (2025);
Rana et al. (2024)
MOD2:
Organizational
Culture
Shared values and beliefs shaping AI
orientation: Innovation culture
(experimentation, risk-taking); Digital
maturity (technology acceptance);
Learning orientation (adaptability,
feedback integration)
Sarfraz et al.
(2026); Sposato &
Dittmar (2026);
Zhang et al. (2025);
Arias-Pérez et al.
(2023)
MOD3:
Institutional
Environment
External context conditions: Regulatory
intensity (legal framework strictness);
Industry norms (sectoral practices);
Cultural context (national values,
uncertainty avoidance)
Chakraborty &
Kumar (2026);
Chen & Dong
(2025); Bag et al.
(2023); Du & Xie
(2021)
Control
Variables
C1: Firm Size
Organizational scale: Log of total
assets; Log of number of employees;
Revenue brackets
Sposato & Dittmar
(2026); Chatterjee
et al. (2021); Bag et
al. (2021)
C2: Firm Age
Organizational maturity: Years since
founding; Organizational life cycle
stage
Islam et al. (2026);
Chen & Dong
(2025); Arias-Pérez
et al. (2023)
C3: Industry
Sectoral context: Industry classification
dummies; Technology intensity;
Regulatory exposure
Liyanage et al.
(2025); Bag et al.
(2023); Du & Xie
(2021)
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Variable Type
Variable Name
Definition/Measurement
Supporting
Literature
C4: Country
National context: Legal origin
(common vs. civil law); Economic
development level; Cultural dimensions
Chakraborty &
Kumar (2026);
Chen & Dong
(2025); Fang et al.
(2023)
Empirical Review
AI and Institutional Dynamics
A substantial stream of research examines AI through the lens of institutional theory. Rudko et al. (2025) provide
a foundational theoretical analysis, arguing that new institutionalism offers resources for understanding AI as
both a product and a producer of institutional forces. They formulate research questions that address AI's
institutional significance, including how AI technologies are legitimized and how they reconfigure institutional
environments.
Reis and Pinheiro (2025) integrate institutional theory with diffusion of innovation theory to understand AI
adoption, demonstrating how coercive, normative, and mimetic pressures drive organizational conformity. Their
theoretical framework distinguishes among adopter categories (innovators, followers, traditionalists) and
emphasizes how perceived attributes of innovation interact with institutional pressures.
Empirical studies support and extend these theoretical claims. Zhang et al. (2025) find that institutional pressures
positively influence generative AI adoption in enterprise digital platforms, with policy uncertainty and
innovative culture moderating these relationships. Liyanage et al. (2025) reveal the rhetorical power of "AI" in
convincing management to embrace the technology, while documenting a "noticeable discrepancy between the
buzz surrounding AI and its actual use."
Fang et al. (2023) identify three institutional logics, expertise, accessibility, and efficiency, that guide legal
professionals' understanding and use of intelligent technologies. Their analysis reveals contradictory attitudes
and logics, suggesting that institutional logics shape but do not determine professionals' engagement with AI.
Chakraborty and Kumar (2026) examine how healthcare organizations respond to regulatory ambiguity
regarding generative AI, finding that organizations build three forms of trust, ability trust, benevolence trust, and
integrity trust, to secure different forms of legitimacy. Their study positions trust as "an active governance
mechanism rather than an abstract relational quality."
AI as Economic Agent
A second stream theorizes AI through economic and strategic lenses. Humberd and Latham (2026) offer the
most explicit theorization of AI as an organizational agent, extending agency theory to conceptualize AI as
potentially achieving "a level of autonomy and self-determination to be considered an agent of the firm." They
theorize specific forms of monitoring and incentive alignment that might align AI agents with firm interests.
Chatterjee et al. (2021) combine institutional theory and the resource-based view to examine AI-based customer
relationship management, finding that AI-CRM affects firm performance, with variations by firm size, age, and
industry. Their study positions AI as a strategic resource that can generate a competitive advantage when
properly implemented.
Bag et al. (2021) integrate institutional theory and resource-based view to understand big data analytics-powered
AI adoption, revealing how institutional pressures shape resource configuration and technology enablement.
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Sposato and Dittmar (2026) identify four drivers enabling resource-constrained SMEs to overcome adoption
barriers: strategic synchronization, leadership commitment, technology sensing, and institutional bridging.
Chen and Dong (2025) examine AI capabilities and export performance, finding that AI serves as "an
intermediary institutional mechanism" connecting home-country institutional context with host-country cultural
environment. Their study reveals that AI's performance effects vary with provincial market development and
cultural distance.
Socio-material Perspectives on AI Agency
Van Rijmenam and Logue (2021) advance understanding of AI agency through sociomateriality and actor-
network theory, arguing that AI's fundamental difference from and similarity to human agency challenge existing
organization theory. They explore implications for sociomateriality, actor-network theory, institutional theory,
and behavioral theory of the firm.
Leavitt et al. (2021) examine machine learning's epistemological opportunities for organization theory, mapping
machine learning forms onto research modes: supervised learning to deductive research, reinforcement learning
to abductive research, and unsupervised learning to inductive research. They propose that machine learning can
"test and prune midrange theory" and "expand the explanatory spectrum that theory can inhabit."
Phillips (2026) offers a critical assessment of mainstream organization theory's neglect of intelligent
technologies, examining institutional theory, organizational identity, and sensemaking as areas that have not
been sufficiently updated to reflect technology's centrality. He identifies causes, including technology's historical
framing as exogenous and scholars' lack of interest in technology.
Ethics, Trust, and Legitimacy
A fourth stream addresses normative dimensions of AI organizational actorhood. Du and Xie (2021) delineate
three dimensions of AI-enabled products with ethical implications, multi-functionality, interactivity, and
intelligence stage, and develop a multi-layered ethical analysis at product, consumer, and society levels. Their
conceptual framework on AI-related corporate social responsibility highlights the factors that influence firms'
socially responsible actions.
Schilke and Reimann (2025) examine the "transparency dilemma," finding, across 13 experiments, that actors
who disclose their use of AI are trusted less than those who do not. Drawing on micro-institutional theory, they
explain this effect through reduced perceptions of legitimacy, demonstrating that transparency is not
straightforwardly beneficial.
Rana et al. (2024) assess the nexus between generative AI adoption and ethical considerations, finding that
institutional pressures (coercive, normative, mimetic) and ethical principles (fairness, accountability,
transparency, accuracy, autonomy) influence the use of GenAI. Organizational innovativeness moderates the
relationship between AI use and performance.
Alibasic (2025) develops a multi-paradigm ethical framework for hybrid intelligence in blockchain governance,
integrating complexity and institutional theories to address the ethical dimensions of human-AI decision-making
in decentralized systems.
Adoption Barriers and Enablers
Several studies examine factors shaping AI adoption and implementation. Das et al. (2026) identify ten barriers
to AI adoption in personalized marketing, using interpretive structural modeling to reveal hierarchical
relationships, positioning infrastructure and cost constraints as root drivers and ROI uncertainty as dependent
outcomes.
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Islam et al. (2026) explore barriers to agentic AI adoption in Bangladeshi SMEs, identifying eight, including
slow internet speeds, limited knowledge, inadequate infrastructure, and political instability. They propose
solutions spanning awareness programs, infrastructure development, and public-private partnerships.
Arias-Pérez et al. (2023) examine co-innovation in digital platforms and the role of business analytics capability
in organizational agility, finding that external pressure for AI adoption positively moderates the relationship
between analytics capability and agility, contradicting expectations that institutional pressures disrupt technical
integration.
Bag et al. (2023) integrate institutional theory and the dynamic capability view to examine climate change
adaptation capability and firm performance, finding that marketing capability partially mediates this relationship.
METHODOLOGY
Research Design
This study employs a narrative review methodology, appropriate for synthesizing theoretical and conceptual
literature to develop an integrated understanding of a multifaceted phenomenon (Torraco, 2016). Narrative
reviews are particularly suited to topics where theoretical development is nascent and fragmented, enabling
critical synthesis across diverse traditions (Baharom, 2025).
Search Strategy
A systematic search of Web of Science was conducted in March 2026 using the following search string:
("artificial intelligence" OR "AI" OR "machine learning" OR "algorithmic decision making") AND ("organizat
ion*") AND ("institutional theory" OR "organizational theory")
This string was designed to capture literature at the intersection of AI and organization theory while remaining
sensitive to diverse theoretical framings.
Inclusion Criteria
Studies were included if they met the following criteria:
1. Document type: Article or review article
2. Web of Science categories: Management, Business, Business Finance, or Economics
3. Language: English
4. Publication years: 2020–2026
5. Relevance: Explicit or implicit theorization of AI's role in organizations
Screening and Selection
The initial search yielded 93 articles. After applying inclusion criteria and removing duplicates, 47 articles were
retained for analysis. The selection process followed PRISMA guidelines, with screening based on titles and
abstracts, followed by full-text assessment for theoretical relevance.
Data Extraction and Analysis
Data were extracted using a standardized template capturing authors, publication year, journal, theoretical
framework, key concepts, empirical context (if applicable), and main theoretical contributions. Analysis
employed thematic synthesis, iteratively coding and categorizing theoretical themes across the literature. The
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analysis was guided by the research question and sensitizing concepts from institutional theory, agency theory,
and sociomateriality.
FINDINGS
Thematic analysis of the 47 reviewed articles reveals four key findings regarding theorizations of AI as an
organizational actor.
AI as Institutional Actor
Finding 1: AI is increasingly theorized as an institutional actor subject to institutional pressures and capable of
institutional work.
The literature conceptualizes AI as simultaneously shaped by and shaping institutional environments. Rudko et
al. (2025) articulate this duality explicitly, positioning AI as both "product of institutional forces" and
"institutional force in its own right." Studies demonstrate how coercive pressures (regulatory requirements),
normative pressures (professional expectations), and mimetic pressures (competitive imitation) drive AI
adoption across contexts (Reis & Pinheiro, 2025; Zhang et al., 2025; Rana et al., 2024).
Beyond passive institutional conformity, AI systems increasingly perform institutional work, actively shaping
rules, norms, and beliefs. Chakraborty and Kumar (2026) show how healthcare organizations build trust as an
institutional infrastructure for generative AI governance, thereby shaping legitimacy conditions. Fang et al.
(2023) reveal how intelligent technologies trigger tensions that redefine professional boundaries, contributing to
institutional change.
The institutional perspective reveals that AI's organizational actorhood is not technologically determined but
institutionally constituted. Legitimacy emerges as a critical resource for AI systems, with Schilke and Reimann
(2025) demonstrating that AI disclosure erodes trust by reducing perceptions of legitimacy. This finding
challenges purely technical framings, positioning AI's acceptance as fundamentally social and institutional.
AI as Economic Agent
Finding 2: AI is theorized as an economic agent with discretionary decision-making capacity, thereby creating
novel principal-agent dynamics that require governance mechanisms.
Humberd and Latham (2026) provide the most explicit theorization of AI as an economic agent, arguing that AI
systems that achieve sufficient autonomy become "agents of the firm" and require alignment mechanisms
analogous to those governing human agents. This extension of agency theory challenges traditional assumptions
that agents must be human, instead focusing on functional characteristics such as discretion, goal orientation,
and decision authority.
Studies examining AI's strategic role reinforce this conceptualization. Chatterjee et al. (2021) position AI-CRM
as a strategic resource enabling automated B2B relationship decisions "without any human intervention." Chen
and Dong (2025) conceptualize AI capabilities as institutional mechanisms that connect home- and host-country
contexts. Bag et al. (2021) demonstrate how AI adoption enables sustainable manufacturing and circular-
economy capabilities.
The agency perspective raises fundamental governance questions. If AI acts as agent, what monitoring
mechanisms ensure alignment with principal interests? Humberd and Latham (2026) theorize "specific forms of
monitoring and incentive alignment" for AI agents, though empirical work on AI governance remains nascent.
Rana et al. (2024) find that ethical principles: fairness, accountability, transparency, accuracy, autonomy,
influence GenAI use, suggesting normative constraints on AI agency.
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AI as Socio-material Assemblage
Finding 3: AI's agency is theorized to emerge from socio-material assemblages rather than as a property of
discrete technologies, thereby challenging methodological individualism in organization theory.
Van Rijmenam and Logue (2021) advance sociomaterial perspectives, arguing that AI agency is distributed
across networks of humans, technologies, and practices. This conceptualization avoids both technological
determinism (AI as autonomous force) and social constructivism (AI as passive tool), instead positioning agency
as an effect of entanglements.
Leavitt et al. (2021) similarly challenge atomistic conceptions, proposing that machine learning can support
theory development precisely because it operates differently from human reasoning. They argue for machine
learning's capacity to "expand the explanatory spectrum" by generating patterns humans might miss while
remaining embedded in human-interpretable frameworks.
Phillips (2026) critiques mainstream organization theory's failure to incorporate intelligent technologies, arguing
that concepts such as organizational identity and sensemaking require reformulation when technologies actively
participate in the constitution of organizations. His analysis suggests that socio-material perspectives offer
resources for updating organization theory.
The socio-material perspective has profound implications for conceptualizing organizational actorhood. Rather
than asking whether AI is an actor, it asks how actorhood is performed through specific configurations of
humans, algorithms, data, and practices. Agency becomes variable and relational rather than fixed and
categorical.
The Autonomy Gradient
Finding 4: Theorizations recognize AI's evolving autonomy, conceptualizing movement along a gradient from
tool to quasi-autonomous actor with implications for organizational control.
Humberd and Latham (2026) explicitly theorize AI's evolution from mimicking human routines toward
autonomy and self-determination, identifying a threshold at which AI becomes "considered an agent of the firm."
This temporal dimension acknowledges that AI's organizational role is not static but develops as capabilities
advance.
Islam et al. (2026) examine the adoption of "agentic artificial intelligence" (AAI) in SMEs, defining AAI as "an
advanced evolution of generative AI" with enhanced autonomy and knowledge-sharing capacity.
Their study reveals that AAI adoption barriers differ from those for earlier forms of AI, suggesting that increasing
autonomy introduces new adoption challenges.
Zhang et al. (2025) examine generative AI adoption in enterprise platforms, finding that institutional pressures
operate differently for generative AI than for earlier technologies.
The generative AI's capacity to produce novel content introduces distinct governance challenges related to output
uncertainty and accountability.
The autonomy gradient concept helps reconcile seemingly contradictory findings. Studies emphasizing AI as an
institutional product (Rudko et al., 2025) and AI as an agentic force (Humberd & Latham, 2026) may describe
different points on the autonomy continuum rather than competing claims. This temporal-relational framing
positions AI actorhood as emergent rather than binary.
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Summary of Findings
Table 2: Summary of Findings
Theme
Key Authors
Theoretical Implications
Institutional
Actor
Rudko et al. (2025);
Reis & Pinheiro
(2025); Zhang et al.
(2025)
Extends institutional
theory to non-human
entities
Economic Agent
Humberd & Latham
(2026); Chatterjee et
al. (2021)
Challenging
anthropocentrism in
agency theory
Socio-material
Assemblage
Van Rijmenam &
Logue (2021); Leavitt
et al. (2021)
Relational ontology of
actorhood
Autonomy
Gradient
Humberd & Latham
(2026); Islam et al.
(2026)
Temporal-relational
conceptualization
DISCUSSION
Theoretical Implications
This review's findings carry significant implications for organizational theory.
First, they challenge the anthropocentric foundations of organization theory. As Phillips (2026) argues,
mainstream organization theory has largely failed to incorporate intelligent technologies, rendering core
concepts potentially obsolete. The reviewed literature suggests that foundational constructs: agency, actorhood,
legitimacy, identity, require reformulation for contexts where non-human entities participate in organizing. This
is not merely an incremental theoretical adjustment but a potential paradigm shift.
Second, they reveal the need for multi-theoretical integration. No single theoretical tradition adequately captures
AI organizational actorhood. Institutional theory illuminates legitimacy and isomorphic dynamics but under-
theorizes AI's autonomous qualities. Agency theory addresses governance and alignment but presumes
anthropocentric agent models. Sociomateriality offers relational ontologies but provides limited guidance for
empirical research. The proposed conceptual framework (Section 4) suggests how these perspectives might
complement rather than compete, with different dimensions capturing different facets of AI actorhood.
Third, they problematize binary conceptualizations of agency. The autonomy gradient finding suggests that AI
actorhood is not binary (actor vs. non-actor) but continuous and developmental. This has methodological
implications: research must attend to how AI's organizational role evolves as capabilities advance, requiring
longitudinal and process-oriented approaches.
Fourth, they extend the application of institutional theory to technology. Rudko et al. (2025), Chakraborty and
Kumar (2026), and others demonstrate the value of institutional theory for understanding AI while also revealing
where the theory requires extension, particularly in conceptualizing technology's active role in institutional work
and change.
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Fifth, they raise normative questions about accountability and responsibility. If AI acts as an organizational
agent, who bears responsibility for its decisions? Humberd and Latham (2026) raise but do not fully resolve
these questions. Schilke and Reimann's (2025) finding that AI disclosure erodes trust suggests that legitimacy
challenges may intensify as AI's agentic role becomes more transparent.
Sixth, the proposed conceptual framework offers a testable model for future research. By specifying five
antecedents, three mediators, three moderators, and four control variables, the framework provides clear
guidance for empirical investigation of AI organizational actorhood. The mediating roles of legitimacy, trust,
and decision rights suggest mechanisms through which antecedents translate into actorhood outcomes, while
moderators identify boundary conditions.
Practical Implications
For managers and organizational leaders, recognizing AI as an emerging organizational actor entails several
practical implications.
Governance mechanisms require reconsideration. If AI systems exercise discretion and make decisions with
organizational consequences, traditional governance focused on human decision-makers becomes insufficient.
Humberd and Latham's (2026) extension of agency theory suggests the need for monitoring and alignment
mechanisms adapted to AI agents. This might include algorithmic auditing, embedding design requirements that
reflect organizational values, and clear accountability structures.
Legitimacy management becomes more complex. Schilke and Reimann (2025) demonstrate that AI disclosure
can reduce trust, suggesting organizations face a transparency dilemma. Managers must navigate disclosure
decisions while building legitimacy through alternative means, perhaps emphasizing ability and benevolence
alongside transparency (Chakraborty & Kumar, 2026).
Workforce development must address human-AI collaboration. As AI assumes agentic roles, human roles
transform from direct task execution to supervision, interpretation, and collaboration with AI systems. Sposato
and Dittmar (2026) identify strategic capability development as crucial for SMEs navigating AI adoption.
Institutional strategy should account for AI's dual role. Organizations shape and are shaped by AI's institutional
environment. Proactive engagement with regulators, industry associations, and professional bodies can help
shape legitimacy conditions favorable to responsible AI deployment.
Ethical frameworks must be embedded in AI governance. Du and Xie (2021) and Rana et al. (2024) demonstrate
that ethical considerations: fairness, accountability, transparency, accuracy, and autonomy influence AI adoption
and outcomes. Organizations should develop ethical AI frameworks that guide the design, deployment, and
monitoring of AI systems.
For policymakers, the findings suggest the need for governance frameworks addressing AI's organizational role.
Current regulatory approaches often treat AI as a tool or product rather than an organizational actor with
emergent agency. Frameworks addressing algorithmic accountability, transparency requirements, and liability
regimes may need updating.
Limitations
This review has several limitations. First, its focus on management, business, and accounting literature may
exclude relevant contributions from computer science, information systems, and science and technology studies.
Second, the search strategy emphasizing institutional and organizational theory terms may under-capture
literature theorizing AI through other frameworks. Third, the narrative review methodology, while appropriate
for theoretical synthesis, involves interpretive choices that other reviewers might make differently. Fourth, the
rapid evolution of AI capabilities means theoretical understandings may lag technological developments. Fifth,
the proposed conceptual framework, while grounded in the literature, requires empirical validation.
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CONCLUSION AND RECOMMENDATIONS
Conclusion
This narrative review has examined how artificial intelligence is theorized as an organizational actor within
contemporary management and organization studies. Synthesizing 47 articles published between 2020 and 2026,
the review reveals that AI is increasingly conceptualized through multiple theoretical lenses: institutional theory,
agency theory, and sociomateriality, each illuminating different dimensions of its organizational role. Four key
themes emerge: AI as an institutional actor subject to institutional pressures and capable of institutional work;
AI as an economic agent creating novel principal-agent dynamics requiring governance mechanisms; AI's agency
as emergent from socio-material assemblages rather than properties of discrete technologies; and AI's evolving
autonomy along a gradient from tool to quasi-autonomous actor. Building on these findings, the review proposes
a comprehensive conceptual framework specifying five antecedents, three mediators, three moderators, and four
control variables that collectively shape AI organizational actorhood as the dependent variable. These findings
collectively suggest that organizational theory stands at an inflection point, where integrating intelligent
technologies into organizational life challenges anthropocentric assumptions embedded in foundational theories
and creates opportunities for theoretical renewal. The question is no longer whether AI will be an organizational
actor but how its actorhood will be theorized, governed, and enacted, a challenge that requires engaging seriously
with technology while maintaining organization theory's distinctive contributions to understanding institutions,
power, meaning, and organizing processes.
Recommendations for Future Research
1. Empirically validate the proposed conceptual framework. Future research should operationalize and test the
relationships specified in the framework using survey, archival, or mixed methods. Scale development for
AI organizational actorhood as a multidimensional construct is urgently needed.
2. Investigate the mediating mechanisms. Longitudinal studies examining how legitimacy, trust, and decision
rights mediate the effects of antecedents on actorhood outcomes would illuminate the processes by which
AI becomes an organizational actor.
3. Examine moderating boundary conditions. Comparative research across different governance structures,
organizational cultures, and institutional environments would reveal how contextual factors strengthen or
weaken relationships in the framework.
4. Develop mid-range theories of AI organizational actorhood. Future research should move beyond broad
theoretical statements toward testable propositions linking specific AI capabilities to organizational
outcomes. Sposato and Dittmar's (2026) propositions offer a model for such work.
5. Investigate the autonomy gradient empirically. Longitudinal studies tracing how AI's organizational role
evolves as capabilities advance would illuminate the transition from tool to agent. Comparative case studies
of organizations at different AI maturity stages could reveal factors that enable or constrain this transition.
6. Examine governance mechanisms for AI agents. Building on Humberd and Latham (2026), research should
investigate what monitoring and alignment mechanisms effectively align AI agents with organizational
interests. Design science approaches might be used to develop and test governance protocols.
7. Study institutional work by and for AI. How do AI systems actively shape their institutional environments?
How do organizational actors perform institutional work to legitimize AI? Ethnographic and process studies
could illuminate these dynamics.
8. Investigate cross-cultural variation in AI actorhood. Chen and Dong's (2025) finding that AI's effects on
export performance vary with cultural distance suggests that AI actorhood may be culturally contingent.
Comparative research across institutional contexts could reveal how national cultures shape AI's
organizational role.
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9. Examine the ethical implications of AI agency. As AI exercises increasing discretion, questions of
accountability, responsibility, and moral agency become pressing. Interdisciplinary research that engages
philosophy, law, and computer science alongside organizational theory is needed.
10. Develop methods appropriate for studying socio-material assemblages. If agency is distributed across
human-technology configurations, methods must capture these dynamics. Leavitt et al.'s (2021) exploration
of machine learning's epistemological opportunities suggests possibilities for methodological innovation.
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