Artificial Intelligence in Business Strategy Research: A Comprehensive Review of Organizational Implications and Emerging Theoretical Directions

Artificial intelligence has emerged as a transformative force in business strategy research, reshaping how organizations conceptualize competitive advantage, reconfigure capabilities, and restructure decision architectures. This integrative review synthesizes peer-reviewed studies to map the evolving role of AI in strategic management. Drawing on literature from leading journals in strategy, information systems, and innovation, the analysis examines how AI is conceptualized—as both a decision-support tool and an autonomous strategic actor—and evaluates its organizational implications across adoption, governance, and transformation processes. Key findings reveal convergences around AI’s augmentation of dynamic capabilities and competitive positioning, yet persistent tensions exist regarding automation-augmentation paradoxes, managerial role erosion, and ethical governance challenges. The review introduces the AI Strategic Organizational Integration Model, a novel synthesis framework comprising five interconnected domains that organize prior research and highlight pathways toward emerging theoretical directions. By classifying studies along dimensions of strategic cognition, capability transformation, organizational redesign, governance tensions, and market-level outcomes, the model illuminates gaps in longitudinal evidence and cross-level theorizing. This work advances an integrative understanding of AI’s strategic significance while offering a structured foundation for future research on intelligent systems in dynamic business environments.

The rapid proliferation of artificial intelligence technologies has fundamentally altered the landscape of business strategy research [1], prompting scholars to reconsider core assumptions about competitive advantage, organizational design, and managerial agency [2]. Between 2017 and 2023, a substantial body of peer-reviewed work emerged that positions AI not merely as a technological innovation but as a strategic lever [3] capable of redefining how firms sense opportunities, seize resources, and sustain performance in volatile markets [4]. Early contributions framed AI primarily through the lens of operational efficiency and predictive analytics [5]. In contrast, more recent studies emphasize its deeper integration into strategic decision-making processes [6] and the reconfiguration of entire business models [7]. This evolution reflects a broader shift from viewing AI as an efficiency-enhancing tool to recognizing it as a catalyst for capability transformation [8] and organizational restructuring [9].

Central to this body of research is the question of how AI adoption influences organizational implications at multiple levels. Scholars have documented how AI systems reshape managerial roles [2] by automating routine judgment tasks while simultaneously demanding new forms of human-AI collaboration [10]. Studies highlight the dual potential of AI to augment human cognition and to substitute for traditional managerial functions [3], generating what has been termed the automation-augmentation paradox. At the firm level, AI is increasingly linked to the development of dynamic capabilities [11] that enable firms to adapt to digital disruption and achieve sustainable competitive advantage [12]. Yet these benefits are not automatic; they depend on strategic governance mechanisms [1] that address issues of control, ethics, and accountability [13].

The organizational transformation associated with AI extends beyond internal processes to encompass broader restructuring of hierarchies, decision architectures, and inter-functional coordination [14, 15]. For instance, research demonstrates that AI-enabled strategic planning can flatten decision-making structures [10] and redistribute authority across levels, while simultaneously introducing new governance challenges related to data privacy [16], algorithmic bias [17], and managerial oversight. These transformations are particularly salient in the context of servitization and digital business models [18], where AI facilitates the shift from product-centric to outcome-based value propositions [19].

Theoretical lenses employed in this literature have diversified considerably. Dynamic capabilities theory remains dominant, providing a framework for understanding how AI augments sensing, seizing, and transforming processes [11, 20]. Complementary perspectives draw on the resource-based view to explain AI-driven competitive advantage [8], while institutional and socio-technical theories illuminate barriers to adoption [21] and the role of organizational culture [22]. More recent work incorporates behavioral and cognitive perspectives to examine how AI influences strategic cognition [23] and judgment under uncertainty [24].

Despite these advances, the literature reveals notable gaps and tensions. First, much of the empirical base remains cross-sectional, limiting insights into the long-term organizational consequences of AI integration [9, 25]. Second, there is a relative scarcity of studies that bridge micro-level managerial experiences with macro-level strategic outcomes [3, 26]. Third, while competitive advantage and capability development receive substantial attention, the dark side of AI—including potential deskilling [27], power asymmetries [28], and ethical dilemmas—remains underexplored in strategic management contexts. These omissions underscore the need for more integrative theorizing that connects technological affordances with organizational and strategic realities.

This integrative review addresses these issues by systematically synthesizing the literature on artificial intelligence in business strategy research published between 2017 and 2023. It focuses explicitly on organizational implications and emerging theoretical directions, classifying studies into coherent thematic domains and comparing theoretical perspectives across contributions. By doing so, the review not only maps the current state of knowledge but also proposes an original synthesis architecture—the AI Strategic Organizational Integration Model (ASOIM)—that organizes prior findings and illuminates pathways for future inquiry. In an era where AI is no longer peripheral but central to strategy formulation and execution [1], such synthesis is essential for guiding both scholarly research [6] and managerial practice [29].

This study adopts a structured integrative review approach to synthesize the rapidly evolving literature on artificial intelligence in business strategy. Unlike purely narrative reviews, the integrative method enables the classification of studies into thematic domains, the comparison of theoretical perspectives, and the identification of convergences, tensions, and omissions across contributions [4, 9]. The review process followed a systematic yet flexible protocol designed to ensure relevance, quality, and comprehensiveness while remaining bounded by the approved reference corpus.

Literature identification began with targeted keyword searches combining core terms such as “artificial intelligence,” “machine learning,” “strategic management,” “business strategy,” “organizational implications,” “dynamic capabilities,” and “competitive advantage.” These searches were executed across major academic databases, including Web of Science, Scopus, and Google Scholar, with additional manual screening of leading journals specified in the protocol: Strategic Management Journal, Academy of Management Review, MIS Quarterly, Journal of Business Research, Information & Management, Technological Forecasting and Social Change, and Organization Science. Only peer-reviewed journal articles and high-quality conference proceedings later published in journals (2017–2023 inclusive) were considered; preprints without subsequent peer-reviewed versions were excluded.

Inclusion criteria required that each study (a) explicitly addressed AI in the context of business strategy or strategic management, (b) discussed at least one of the focal themes (strategic decision-making, organizational implications, capability development, governance, or theoretical perspectives), and (c) offered conceptual, empirical, or review-based insights directly relevant to organizational-level analysis. Exclusion criteria eliminated purely technical AI papers without strategic implications, studies focused solely on consumer or societal levels, and research published outside the 2017–2023 window. Quality screening further prioritized publications appearing in journals with established impact in digital business and management studies.

Following iterative screening and cross-validation, the final corpus, which collectively represents the most influential and directly aligned contributions, was selected. These studies span conceptual frameworks, literature reviews, and qualitative/quantitative empirical investigations, providing a balanced foundation for synthesis.

The reviewed literature reveals a clear maturation in how AI is conceptualized in business strategy research, reflecting broader shifts in technological capabilities and scholarly maturity. Early studies (2017–2019) predominantly framed AI as a predictive tool that supports human judgment in strategic planning, treating intelligent systems as sophisticated instruments that enhance rather than replace managerial discretion [23, 24]. By contrast, post-2020 contributions increasingly portray AI as an active strategic actor capable of autonomous sensing and decision execution. This shift carries profound implications for theories of agency and organizational control [3, 8]. This conceptual shift is accompanied by growing recognition that AI operates along a continuum—from narrow task automation to broader cognitive augmentation—generating both opportunities and paradoxes for organizations as they seek to harness AI’s potential without ceding strategic direction to opaque algorithmic processes [2, 3].

Organizational implications of AI adoption emerge as a central theme, cutting across functional boundaries and hierarchical levels. Multiple studies document how AI implementation triggers significant restructuring of workflows, hierarchies, and resource allocation, often disrupting established power dynamics and requiring fundamental rethinking of coordination mechanisms [14, 15, 19]. For example, AI-driven analytics have been shown to enhance process-oriented dynamic capabilities, thereby improving firm performance in servitized contexts where value creation depends on integrating products with data-enabled services [12, 18]. At the same time, adoption introduces risks related to employee resistance, skill obsolescence, and cultural misalignment, particularly among SMEs navigating digital transformation with fewer slack resources and less specialized expertise than their larger counterparts [22, 25].

The impact of AI on strategic decision-making and managerial roles is another prominent domain in the literature, lying at the intersection of behavioral strategy and organizational design. Research consistently highlights the reconfiguration of decision architectures, with AI systems enabling faster, data-driven choices while challenging traditional notions of managerial authority and intuition that have long been central to strategic leadership [10, 16]. The automation-augmentation paradox is particularly salient here: while AI can substitute for routine cognitive tasks, thereby streamlining decision-making, it simultaneously elevates the importance of human judgment in ambiguous, high-stakes strategic contexts where nuance, ethical reasoning, and stakeholder considerations remain beyond AI’s purview [3, 27]. This duality raises critical questions about the evolving identity of managers in AI-augmented environments, including whether managerial roles will shift toward oversight and exception handling or become increasingly marginalized as algorithms assume greater decision-making authority [1, 2].

AI-enabled capabilities and competitive advantage receive extensive attention across the corpus, reflecting the enduring centrality of performance outcomes in strategic management scholarship. Scholars drawing on dynamic capabilities theory demonstrate that AI competencies—encompassing data analytics, machine learning integration, and algorithmic agility—mediate the relationship between technological investment and sustained performance, positioning AI not as a direct driver of advantage but as an enabler of organizational capacities to sense, seize, and transform [11, 20, 26]. Studies further illustrate how AI fosters novel sources of competitive advantage through network effects, personalized offerings, and circular business model innovation, suggesting that AI’s strategic value lies less in efficiency gains than in enabling entirely new value propositions and revenue models [6, 8, 13]. However, these advantages are contingent upon complementary organizational factors such as absorptive capacity and strategic alignment, reinforcing the relational view that AI’s value emerges from its integration within broader organizational systems rather than from technological ownership alone [18, 21].

Governance, transformation, and restructuring represent an emerging yet critical stream within the literature, one that has gained prominence as early AI adopters have encountered unanticipated risks and implementation failures. The literature underscores the necessity of board-level oversight, CIO involvement, and ethical frameworks to mitigate risks associated with algorithmic opacity and power concentration, recognizing that governance structures designed for conventional information technology may be inadequate for systems capable of autonomous decision-making [1, 7, 17]. Organizational transformation is portrayed as both structural (e.g., flatter hierarchies, cross-functional teams) and cultural (e.g., ambidexterity between efficiency and innovation), suggesting that successful AI integration demands simultaneous attention to formal organizational arrangements and the informal norms that shape how employees interpret and respond to AI-driven changes [28]. Collectively, these themes illustrate that AI’s strategic value is realized only when technological deployment is synchronized with deliberate organizational redesign, positioning implementation as a strategic endeavor rather than a purely technical one [9, 29].

Across the corpus, theoretical perspectives converge around dynamic capabilities and the resource-based view. Yet, tensions persist regarding the relative emphasis on substitution versus augmentation logics, reflecting deeper disagreements about the relationship between technology and human agency [3, 11]. The synthesis also reveals temporal evolution: pre-2020 studies focused more on potential benefits, reflecting the optimism characteristic of emerging technology research, while recent work increasingly addresses implementation barriers, dark-side effects, and long-term strategic governance, signaling a more nuanced and critical turn in the literature [27]. Omissions include limited cross-industry comparative analyses to test the boundary conditions of existing findings and insufficient attention to non-Western contexts, where institutional environments, cultural norms, and developmental trajectories may produce distinct patterns of AI adoption and organizational responses [25].

Table 1 summarizes the five recurring thematic domains, their principal organizational implications, dominant theoretical anchors, and the major tensions and gaps that persist across the reviewed literature.

These patterns set the stage for the integrative architecture presented in the following section, which seeks to transcend the fragmentation evident across these thematic domains by offering a unified framework capable of accommodating both the promise and the perils of AI integration in strategic management.

To organize the heterogeneous literature into a coherent architecture, this review introduces the AI Strategic Organizational Integration Model (ASOIM). The ASOIM is an original synthesis schema comprising five interconnected domains that capture the multifaceted nature of AI in business strategy research while highlighting relational pathways among technologies, organizational processes, and strategic outcomes. The model is deliberately non-causal and non-propositional; its purpose is integrative—serving as a conceptual map that classifies prior studies, reveals convergences and tensions, and illuminates trajectories for future theoretical development. Figure 1 visualizes this integrative architecture by mapping the five domains around a central AI hub and showing the reciprocal relationships, convergence zones, and unresolved tensions that structure the literature.

The five synthesis dimensions are: (1) AI Conceptualization in Strategy Research – encompassing studies that define AI as decision support versus autonomous strategic actor [4, 23, 24]; (2) Capability Transformation Mechanisms – focusing on how AI augments or substitutes dynamic capabilities and generates competitive advantage [11, 12, 20, 26]; (3) Organizational Redesign Effects – addressing restructuring of hierarchies, decision architectures, and managerial roles [10, 14, 15, 19]; (4) Governance and Control Tensions – examining ethical, oversight, and accountability challenges [1, 7, 16, 17]; and (5) Market and Competitive Implications – exploring broader outcomes such as servitization, business model innovation, and industry-level shifts [6, 8, 13, 18].

The five domains of the ASOIM are not isolated silos but form a tightly interconnected web that reveals both synergies and persistent tensions across the reviewed literature. Studies in AI conceptualization (domain 1) directly inform capability transformation mechanisms (domain 2), as the shift from AI-as-tool to AI-as-actor fundamentally alters how firms build dynamic capabilities [4, 11, 23]. For instance, when AI is positioned as an autonomous strategic actor, it accelerates the sensing and seizing processes central to dynamic capabilities theory, yet simultaneously introduces substitution effects that challenge traditional resource-based advantage [3, 8, 20].

Organizational redesign effects (domain 3) are inextricably linked to governance and control tensions (domain 4). AI-enabled flatter decision architectures demand new oversight structures. Yet, the same literature shows that without robust board-level and CIO involvement, algorithmic opacity can erode managerial control and amplify ethical risks [1, 7, 10, 16]. This interconnection produces the automation-augmentation paradox: capability gains in domain 2 are realized only when redesign (domain 3) and governance (domain 4) keep pace, otherwise competitive implications (domain 5) shift from advantage to vulnerability [2, 3, 27].

Market and competitive implications (domain 5) act as both outcome and feedback loop, feeding back into renewed conceptualization. Servitization and business-model innovation driven by AI competencies illustrate how capability transformation translates into sustained advantage, but only when governance tensions are resolved [6, 12, 13, 18]. Temporal analysis of the corpus further shows evolution: pre-2020 emphasis on potential benefits (domains 1–2) has given way to post-2020 focus on implementation frictions and dark-side effects (domains 3–4), underscoring the iterative nature of AI integration [25]. These cross-theme linkages highlight convergences around dynamic capabilities as the dominant theoretical anchor while exposing omissions in multi-level and longitudinal evidence [9, 26, 29].

This integrative review advances business strategy research by consolidating and extending three core theoretical streams that have historically developed in parallel rather than in concert. First, it refines dynamic capabilities theory by demonstrating how AI competencies constitute a distinct meta-capability that augments sensing, seizing, and transforming in digital contexts, moving beyond Teece’s original framework to incorporate machine-augmented cognition. Rather than viewing AI as merely another technological input, this reconceptualization positions AI as fundamentally reshaping how organizations perceive environmental shifts, act on opportunities, and reconfigure internal resources, previously constrained by human cognitive limits [11, 20]. Second, it deepens the resource-based view by positioning AI not as a static resource but as a relational capability whose value emerges only through complementary organizational redesign and governance, thereby addressing long-standing calls for process-oriented extensions that account for how resources are deployed rather than which resources are possessed [8, 21]. Third, the review introduces the automation-augmentation paradox as a central theoretical tension, bridging behavioral strategy and socio-technical systems perspectives to explain why AI simultaneously substitutes routine tasks and elevates the strategic importance of human judgment, revealing a generative friction that organizations must actively manage rather than resolve [2, 3, 27]. By synthesizing these lenses within the ASOIM, the review resolves fragmentation across journals. It provides a unified architecture for future theorizing that transcends single-theory applications, offering scholars a coherent framework that integrates micro-level cognitive dynamics, meso-level organizational structures, and macro-level institutional pressures [4, 9]. Collectively, these contributions shift strategic management scholarship from technology-adoption narratives toward a more mature understanding of intelligent systems as endogenous drivers of organizational evolution, wherein AI is not simply adopted but becomes constitutive of strategy itself.

The synthesized literature offers actionable insights for executives navigating AI adoption across diverse organizational contexts. Managers should prioritize capability audits that assess both technological readiness and complementary human-AI collaboration structures to avoid the automation-augmentation paradox, ensuring that efficiency gains from automation do not inadvertently undermine the very human expertise required to manage complex strategic decisions [1, 3, 10]. Boards and CIOs are urged to establish cross-functional AI governance committees early in implementation to align strategic oversight with operational redesign, thereby embedding accountability and ethical considerations into the technology’s deployment rather than addressing them reactively after problems emerge [7, 16, 17]. To gain a competitive advantage, firms should invest in absorptive capacity and data-network effects rather than isolated AI tools, particularly when pursuing servitization or circular business models in which value derives from integrated solutions and closed-loop systems rather than discrete product offerings [6, 12, 13, 18]. SMEs, often underrepresented in prior work, can leverage AI to mitigate risks and enhance agility, provided cultural ambidexterity and skill-upgrading programs accompany deployment, enabling these smaller organizations to balance exploration of new AI-enabled opportunities with exploitation of existing operational strengths [22, 25]. Overall, the reviewed studies converge on the message that AI’s organizational value is realized through deliberate integration rather than isolated deployment, offering executives a roadmap for transforming managerial roles, decision architectures, and competitive positioning that treats technology and organization as co-evolving rather than as separate domains [2, 14, 29].

Figure 2 provides a real-world representation of AI integration as an organizational system, illustrating how strategic decision-making emerges from the interaction between executive control, business functions, AI models, data infrastructures, and market feedback, all mediated by continuous human–AI collaboration and governance oversight.

This synthesis is delimited to the approved corpus of peer-reviewed sources (2017–2023), which, while comprehensive within the specified scope, excludes pre-2017 foundational work and post-2023 developments that might provide additional historical context or capture more recent generative AI advancements. The focus on high-impact strategy and information systems journals may underrepresent contributions from emerging outlets or from non-Western contexts where alternative conceptualizations of technology and organization may prevail [25]. As an integrative rather than meta-analytic review, quantitative effect sizes are not aggregated; interpretive synthesis necessarily involves selective emphasis on dominant themes, and alternative thematic organizations could yield different insights. Finally, the ASOIM, while original, remains a conceptual organizing device rather than an empirically validated framework, and its utility as an analytical tool awaits systematic testing across varied empirical settings.

Future research should address the identified gaps through longitudinal, multi-level studies that track AI’s organizational consequences over time and across hierarchies, capturing how strategic decisions made at executive levels translate into operational routines and how, in turn, bottom-up adaptations reshape formal strategy [9, 26]. Cross-cultural and cross-industry comparative designs are needed to test the generalizability of capability transformation and governance mechanisms, examining whether patterns observed in Western technology-intensive sectors hold across varied institutional environments and industry structures [22, 25]. Theoretical advancement requires integrating behavioral strategy with ethics and institutional theory to examine power asymmetries and algorithmic accountability, moving beyond efficiency-focused analyses to consider how AI systems may concentrate decision-making authority, embed bias, and challenge established norms of managerial responsibility [3, 27]. Scholars should also explore hybrid human-AI strategic cognition and the role of generative AI in business model innovation, extending the ASOIM into new empirical contexts where the boundaries between human judgment and machine output are increasingly blurred [4, 6, 13]. Such directions will further illuminate how intelligent systems reshape the very foundations of strategic management, challenging assumptions about where organizational knowledge resides, how strategic decisions are legitimized, and what forms of competitive advantage remain sustainable in an era of increasingly capable and accessible AI.

Artificial intelligence has moved from peripheral technology to core strategic imperative, compelling organizations to rethink capabilities, governance, and competitive logic. By synthesizing the literature through the ASOIM, this review clarifies organizational implications while charting a coherent path for theoretical progress. As AI continues to evolve, the frameworks and insights presented here equip both scholars and practitioners to navigate the next wave of intelligent strategy with greater clarity and foresight.

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Chinedu Okafor & Amina Bello contributed to this work.

Authors and affiliations

Department of Digital Business and Analytics, University of Lagos, Lagos, Nigeria
Chinedu Okafor & Amina Bello

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Correspondence to Chinedu Okafor

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