The emergence of an artificial intelligence (AI)-mediated economy is fundamentally altering the nature of corporate strategy. Traditional models of strategic management, rooted in resource-based and dynamic capabilities views, are being challenged by the pervasive integration of AI into organizational processes. This theory-development article synthesizes recent scholarship to propose a new theoretical lens on how AI reshapes corporate strategy and firm competitiveness. We argue that AI acts not merely as a tool but as a strategic mediator that reconfigures firm boundaries, decision architectures, and capability development pathways. By examining the transition from conventional strategic planning to AI-mediated strategic coordination, the paper highlights the organizational implications for competitiveness, including enhanced decision speed, adaptive capability reconfiguration, and renewed competitive positioning. We develop six theoretical propositions that articulate the causal relationships between AI adoption, strategic control mechanisms, and competitive outcomes. The framework underscores the conditions under which AI strengthens or undermines firm competitiveness, offering implications for managers and theorists alike. This work contributes to strategic management and digital business literature by providing an integrated theory of AI-mediated strategy that addresses the gap in understanding corporate-level adaptations in intelligent economies.
In today’s business landscape, artificial intelligence (AI) is no longer a peripheral technology but a core mediator of economic activity and organizational life [1, 2]. The concept of an AI-mediated economy refers to a market environment in which intelligent systems permeate decision-making, resource allocation, and value-creation processes across industries [3-8]. This shift has profound implications for corporate strategy, traditionally defined as the determination of a firm’s long-term goals and the adoption of courses of action and the allocation of resources necessary to carry them out. However, in an AI-mediated context, strategy evolves from a static planning exercise to a dynamic, algorithmically supported coordination process that blurs the lines between human and machine agency [4, 7].
The acceleration of AI adoption has been driven by advances in machine learning, big data analytics, and generative AI, enabling firms to process vast amounts of information in real time and derive strategic insights that were previously unattainable [9-11]. For instance, companies such as Amazon and Google have leveraged AI not only for operational efficiency but for reshaping entire business models and competitive landscapes [2]. Yet, the literature reveals a critical gap: while much attention has been paid to AI’s technical capabilities and operational applications, fewer studies have explored its systemic impact on corporate-level strategy and firm competitiveness [1, 12, 13].
Traditional strategic management theories, such as the resource-based view and dynamic capabilities, provide foundational insights into how firms achieve sustained competitive advantage through unique resources and the ability to reconfigure them in response to environmental changes [14-18]. However, these frameworks were developed in pre-AI eras and do not fully account for how AI alters the nature of resources (for example, data as a strategic asset), capabilities (for example, AI-enabled sensing and seizing), and competitive dynamics (for example, algorithm-driven market positioning) [2, 15]. Recent scholarship has begun to address this, with studies examining AI as a source of competitive advantage [2], the role of AI in innovation management [8], and the organizational challenges of AI implementation [6, 7].
The implications are particularly salient for firm competitiveness, defined here as the ability to achieve superior performance relative to rivals through unique value creation and capture [2, 13]. AI can amplify this by enabling predictive modeling for market entry, personalized strategies, and rapid iteration [12, 19-26]. Conversely, firms that fail to adapt their corporate strategy risk obsolescence, as evidenced by cases in which traditional retailers struggled against AI-powered e-commerce giants [5, 23]. Organizational conditions such as digital maturity, leadership commitment to AI governance, and cultural readiness moderate these outcomes [7, 20, 24].
Moreover, AI influences firm boundaries by enabling new forms of coordination that may reduce transaction costs and alter make-or-buy decisions [19, 27]. It also affects diversification strategies by allowing for more granular portfolio management based on AI predictions [20]. The organizational implications are multifaceted, encompassing changes in governance and leadership roles (such as the strategic involvement of CIOs and boards [5]) and human-AI collaboration [7, 22]. Literature has highlighted both opportunities—such as data-driven competitiveness and digital transformation [11, 25]—and challenges, including ethical concerns, skill gaps, and potential strategic misalignments if AI systems are not properly governed [6, 22].
This article seeks to bridge these gaps by developing a theory of corporate strategy in an AI-mediated economy. Specifically, it investigates how AI reshapes the foundations of corporate strategy, the transition to AI-mediated strategic coordination, the effects on firm scope, diversification, integration, and strategic control, and the implications for capability development, competitive positioning, and strategic renewal. It further explores the organizational conditions under which AI bolsters or erodes firm competitiveness.
The paper proceeds as follows. First, we synthesize the theoretical foundations and existing literature. Second, we present our original theory development, including six propositions and a conceptual framework. Through this structure, the manuscript contributes to strategic management and digital business studies by offering a cohesive theoretical explanation that extends current understandings and supplies a roadmap for future empirical work and managerial practice.
Corporate strategy in the AI era is evolving from deliberate, top-down planning toward emergent, intelligence-augmented processes. Foundational reviews demonstrate that, over the past four decades, strategic management research has increasingly incorporated AI as a disruptive force that redefines the sources of competitive advantage [1]. Empirical and conceptual work shows that AI alters the very logic of strategy formulation by embedding predictive and prescriptive analytics directly into executive decision cycles [2, 4]. For example, AI enables firms to move beyond static SWOT analyses to continuous, real-time scenario modeling, thereby accelerating strategic foresight [12]. This evolution is not uniform; it depends on industry context, with technology-intensive sectors leading the reconfiguration of strategy processes [8, 10].
Strategic control mechanisms are being transformed by AI through the automation of monitoring, evaluation, and corrective actions. Studies emphasize that AI-driven decision systems allow for decentralized yet synchronized coordination across global operations, reducing reliance on hierarchical command structures [5, 6]. Boards and CIOs play pivotal roles in aligning AI initiatives with corporate objectives, ensuring that intelligent systems support rather than supplant human strategic oversight [5]. Coordination challenges arise when AI introduces opacity (the “black box” problem), necessitating new governance protocols that blend algorithmic transparency with organizational accountability [7, 22].
In intelligent firms, AI compels them to reconfigure dynamic capabilities around data, algorithms, and learning loops. Literature syntheses highlight how AI augments sensing, seizing, and transforming processes, enabling faster adaptation to market shifts [15, 16, 28, 29]. Organizational learning becomes hybrid, with AI facilitating knowledge creation at scale while humans provide contextual judgment [8, 21]. However, capability development is not automatic; it requires deliberate investment in AI readiness frameworks that align technology with human capital [17, 20].
In data- and algorithm-intensive markets, competitiveness increasingly depends on a firm’s capacity to leverage artificial intelligence (AI) not merely as an efficiency-enhancing tool but as a core driver of value creation and strategic differentiation. While early digital transformation efforts emphasized cost reduction and process optimization, contemporary evidence suggests that AI’s primary strategic contribution lies in its ability to generate new forms of user value, reshape market interactions, and enable scalable innovation. Specifically, AI facilitates powerful network effects by continuously learning from user data, improving personalization, and enhancing predictive capabilities. These dynamics reinforce user engagement and platform stickiness, thereby amplifying value creation over time [2, 13, 14].
As a result, the locus of competitive advantage is shifting away from traditional tangible assets—such as physical infrastructure or capital intensity—toward intangible, algorithmic capabilities embedded in data pipelines, machine learning models, and decision systems. Firms that excel in developing and refining these capabilities can create self-reinforcing feedback loops between data accumulation, model improvement, and user experience. This recursive advantage is difficult for competitors to replicate, particularly when supported by proprietary datasets and advanced analytics architectures.
Moreover, firms that integrate AI into their innovation processes demonstrate significantly higher levels of experimentation in business models, products, and services. AI enables rapid prototyping, simulation, and testing, allowing organizations to explore alternative value propositions more quickly and at lower cost. Consequently, such firms are better positioned to orchestrate ecosystems, align complementary actors, and co-create value across organizational boundaries [9, 25]. This ecosystem-centric approach to competitiveness underscores the importance of relational and platform-based strategies in AI-mediated environments.
However, the benefits of AI are neither automatic nor uniformly distributed. Competitiveness can deteriorate when AI adoption remains superficial—limited to isolated use cases or operational improvements—without deeper integration into strategic decision-making and organizational processes. Firms that fail to align AI initiatives with broader strategic renewal risk creating fragmented capabilities, underutilized data assets, and misaligned incentives. Furthermore, overreliance on static models or legacy systems can introduce rigidity, reducing a firm’s ability to adapt to evolving market conditions. Sustained competitiveness, therefore, requires continuous renewal of both technological and organizational capabilities, ensuring that AI systems remain aligned with strategic objectives and environmental demands [23, 26].
AI mediation fundamentally alters the nature of firm boundaries by reducing coordination costs and enabling seamless integration with external actors, including partners, suppliers, and platform participants. Traditional theories of the firm emphasize boundaries as a function of transaction costs and control considerations; however, AI challenges these assumptions by automating coordination, enhancing transparency, and facilitating real-time information exchange across organizational interfaces. As a result, firms are increasingly able to engage in more open, flexible, and dynamic forms of collaboration.
Conceptual models suggest that AI-enabled coordination mechanisms support the expansion of open innovation practices and the adoption of servitization strategies, whereby firms move beyond product offerings to deliver integrated solutions and continuous services [15, 19, 27]. AI systems play a critical role in managing these complex arrangements by processing large volumes of data, optimizing interactions, and enabling adaptive responses to changing conditions. This, in turn, allows firms to reconfigure their value creation activities across a broader ecosystem while maintaining strategic coherence.
Strategic renewal in this context becomes an ongoing, iterative process rather than a discrete transformation event. Firms must continuously adjust their boundaries, reallocate resources, and realign capabilities in response to both internal developments and external pressures. Organizational design choices—such as governance structures, decision rights allocation, and coordination mechanisms—serve as key moderators of this renewal process [18, 29]. For example, decentralized structures may enhance responsiveness and experimentation, while centralized control may support consistency and risk management in AI deployment.
Despite the growing body of research on AI’s impact on individual strategic dimensions—such as innovation, coordination, and governance—a significant gap remains in the literature. Existing studies tend to examine these effects in isolation, without integrating them into a comprehensive framework that links corporate-level reconfiguration to competitiveness outcomes. This fragmentation limits our understanding of how AI reshapes strategy holistically and how different organizational elements interact to produce sustained advantage. Addressing this gap requires a unifying theoretical perspective that captures the interdependencies among AI adoption, organizational design, and competitive performance.
Table 1 distinguishes alternative modes of AI-mediated corporate strategy and demonstrates that competitiveness depends on organizational integration rather than AI adoption alone.
Table 1. Modes of corporate strategic transformation under AI mediation
Strategic mode | Role of AI in the firm | Dominant coordination logic | Boundary configuration | Capability profile | Competitive implication | Principal strategic risk |
Instrumental augmentation | AI is used as a support tool for isolated analytical or operational tasks | Human-led coordination with limited algorithmic input | Traditional boundaries remain largely intact | Localized efficiency capabilities | Marginal performance improvement; weak differentiation | Superficial adoption that automates existing routines without strategic renewal |
Decision-embedded strategy | AI is integrated into strategic analysis, planning, and resource allocation | Hybrid human-AI coordination | Selective permeability of firm boundaries through data-linked partnerships | Enhanced sensing and decision agility | Faster strategic response and improved market positioning | Overconfidence in model outputs and insufficient interpretive oversight |
Platform-coordinated strategy | AI orchestrates interactions across functions, units, and ecosystem actors | Distributed but synchronized algorithmic coordination | More fluid boundaries with ecosystem integration and modular collaboration | Reconfigurable cross-boundary capabilities | Higher diversification potential and ecosystem leverage | Dependency on external platforms, interoperability constraints, and governance complexity |
Adaptive intelligence architecture | AI becomes embedded in ongoing strategic recalibration and organizational redesign | Recursive coordination combining real-time analytics, governance, and learning loops | Dynamically redefined boundaries based on strategic fit and transaction efficiency | Hybrid dynamic capabilities anchored in continuous learning | Sustained competitive advantage through renewal, reconfiguration, and intelligent adaptation | Capability rigidity if learning loops degrade or legacy assumptions become encoded in models |
Misaligned algorithmic expansion | AI adoption expands technically but remains disconnected from corporate intent | Fragmented coordination across technical and managerial domains | Boundary decisions become inconsistent or opportunistic | Disjointed technical capabilities without strategic coherence | Erosion of competitiveness despite AI investment | Strategic drift, bias amplification, fragmented incentives, and weakened control |
Building on the synthesized foundations, we advance a novel theory of corporate strategy in an AI-mediated economy. This theory conceptualizes AI as both a strategic input and a transformative mediator that reshapes how firms coordinate activities, configure capabilities, and define organizational boundaries. Rather than treating AI as a standalone technology, we argue that its strategic significance lies in its embeddedness within the broader corporate architecture.
At the core of this framework is the premise that competitiveness does not arise from AI adoption per se, but from the depth and effectiveness of its organizational integration. Firms that successfully embed AI into their decision systems, governance structures, and innovation processes are better positioned to achieve strategic coherence, adaptability, and sustained advantage. In contrast, firms that adopt AI in a fragmented or misaligned manner may experience diminished returns or even negative performance effects.
The following propositions articulate the key mechanisms through which AI-mediated corporate reconfiguration influences competitiveness:
The depth of integration of AI-mediated decision systems into corporate strategy positively influences strategic agility, enabling faster environmental sensing and response and thereby enhancing firm competitiveness in volatile markets [1, 2, 6, 12].
Deep integration implies that AI is embedded in core strategic processes—such as resource allocation, market analysis, and innovation planning—rather than confined to operational tasks. This integration enhances a firm’s ability to process complex information, detect emerging trends, and respond proactively to environmental changes.
Firms that redesign coordination mechanisms around AI-enabled platforms experience a contraction of firm boundaries and greater ecosystem integration, which in turn supports more effective diversification and raises overall competitiveness [15, 19, 25, 27].
AI-enabled platforms enable modular, scalable coordination across organizational boundaries, allowing firms to access external capabilities and enter new markets more efficiently. This boundary fluidity enhances strategic flexibility and diversification potential.
AI-driven capability reconfiguration strengthens dynamic capabilities when accompanied by complementary investments in organizational learning and governance, leading to sustained competitive advantage; absent these complements, AI may instead create rigidity and erode competitiveness [8, 16, 17, 29].
This proposition highlights the conditional nature of AI’s benefits. Without appropriate learning mechanisms and governance frameworks, AI systems may reinforce existing routines and limit adaptability.
In AI-mediated economies, strategic control shifts from ex post monitoring to real-time algorithmic orchestration, improving resource allocation efficiency and competitive positioning, provided that human oversight prevents algorithmic bias [5-7, 22].
AI enables continuous monitoring and adjustment of organizational activities, reducing delays and inefficiencies associated with traditional control systems. However, effective oversight remains essential to ensure ethical and unbiased outcomes.
The extent of AI adoption moderates the relationship between market dynamism and strategic renewal such that high-AI firms achieve superior renewal outcomes and competitiveness through continuous model refinement and organizational redesign [10, 13, 23, 26].
Firms with advanced AI capabilities are better equipped to navigate dynamic environments by iteratively updating models and adapting organizational structures in response to new information.
Organizational conditions—specifically digital maturity, leadership AI literacy, and cultural openness—determine whether AI-mediated strategy strengthens or weakens firm competitiveness; low-maturity firms risk competitive disadvantage through misaligned implementation [7, 20, 21, 24].
These conditions shape a firm’s ability to absorb, interpret, and effectively deploy AI technologies, thereby influencing the overall impact on competitiveness.
Table 2 specifies the organizational contingencies that determine whether an AI-mediated strategy produces adaptive competitiveness or strategic vulnerability.
Table 2. Organizational contingencies shaping the competitive effects of AI-mediated strategy
Organizational contingency | High-strength condition | Low-strength condition | Mechanism through which it affects the AI-mediated strategy | Likely effect on strategic coordination | Likely effect on competitiveness |
Digital maturity | Integrated data architecture, interoperable systems, reliable digital infrastructure | Fragmented legacy systems, poor interoperability, and weak data pipelines | Determines whether AI outputs can flow into enterprise-wide decision processes | High maturity enables rapid coordination and scalable execution; low maturity creates bottlenecks and fragmented decisions | High maturity strengthens agility and renewal; low maturity reduces returns on AI investment |
Leadership AI literacy | Senior executives and boards understand AI’s strategic, organizational, and governance implications | Leadership treats AI as a purely technical issue delegated downward | Shapes whether AI is aligned with corporate scope, diversification, and control choices | High literacy improves strategic alignment and oversight; low literacy produces a misfit between AI use and corporate objectives | High literacy supports coherent advantage; low literacy increases implementation failure and strategic drift |
Governance quality | Transparent models, accountability protocols, bias monitoring, and human override rights | Opaque systems, unclear ownership, weak ethical controls | Governs whether algorithmic orchestration remains legitimate, controllable, and strategically interpretable | Strong governance stabilizes hybrid decision-making; weak governance undermines trust and control | Strong governance protects advantage durability; weak governance may damage performance and legitimacy |
Organizational learning capacity | Continuous feedback interpretation, experimentation, and cross-functional learning routines | Static routines, low absorptive capacity, weak experimentation culture | Determines whether AI outputs trigger adaptation or merely reinforce old patterns | Strong learning converts feedback into strategic recalibration; weak learning turns AI into rigid automation | Strong learning supports renewal and long-term competitiveness; weak learning promotes rigidity |
Cultural openness to human-AI collaboration | Employees and managers accept hybrid judgment and iterative redesign | Resistance to AI, defensiveness, and siloed decision norms | Affects whether AI recommendations are productively interpreted and incorporated into action | Open cultures enable coordinated adoption; resistant cultures generate friction and underuse | Open cultures amplify AI value; resistant cultures weaken competitive impact |
Boundary-management capability | A firm can strategically decide when to internalize, partner, platformize, or externalize activities | Boundary choices are reactive, inconsistent, or technology-led rather than strategy-led | Influences whether AI reduces coordination costs in ways that support coherent ecosystem positioning | Strong capability enables disciplined ecosystem integration; weak capability creates dependency and loss of control | Strong boundary management improves diversification and resilience; weak boundary management threatens strategic autonomy |
Figure 1 illustrates the recursive architecture of corporate strategy in an AI-mediated economy, showing how AI-enabled decision systems, strategic coordination, and capability reconfiguration interact to produce competitive outcomes.
Figure 1. Corporate strategy architecture in an artificial intelligence-mediated economy.
The figure depicts corporate strategy as a recursive socio-technical architecture in which AI inputs and intelligent decision systems are translated into competitive outcomes through corporate strategic coordination and capability reconfiguration. Feedback loops indicate that performance outcomes continuously refine both AI models and organizational strategy, while organizational enablers and governance conditions moderate the effectiveness of the entire system.
The theory of corporate reconfiguration under artificial intelligence, as articulated through Proposition 4, reveals a profound transformation in strategic control mechanisms. Traditional ex-post monitoring gives way to real-time algorithmic orchestration, whereby AI systems continuously monitor environmental signals, allocate resources, and initiate corrective actions with minimal latency [5-7]. This shift compresses decision cycles from weeks to seconds, enabling firms to maintain tighter alignment between corporate intent and operational execution [4, 22]. Yet the literature consistently cautions that such orchestration demands hybrid governance architectures that safeguard against algorithmic opacity and unintended biases [7, 22]. Boards and chief information officers must therefore institutionalize transparency protocols and ethical guardrails, ensuring that AI-augmented control enhances rather than erodes strategic autonomy [5, 20].
Empirical patterns documented across peer-reviewed studies indicate that firms achieving this balance report superior competitive positioning through optimized capital deployment and reduced coordination costs [2, 13]. Conversely, organizations that delegate control without complementary human-AI protocols risk strategic drift, as algorithmic recommendations may optimize for narrow metrics at the expense of long-term corporate coherence [23, 26]. The organizational implication is clear: strategic control in AI-intensive environments becomes a capability in its own right—one that must be deliberately cultivated through training, process redesign, and cultural acceptance of machine-augmented judgment [17, 24].
Proposition 3 and Proposition 5 together explain how AI compels continuous capability reconfiguration. Dynamic capabilities are no longer solely human-driven; they become hybrid constructs in which AI augments sensing (through predictive analytics), seizing (via automated scenario simulation), and transforming (through automated resource reallocation) [8, 15, 29]. This hybridization accelerates strategic renewal by shortening the feedback loops between market signals and organizational response [10, 16]. Firms that embed AI within learning infrastructures report more frequent and successful business-model experiments, enabling them to pivot diversification portfolios with granular precision [20, 25].
Capability reconfiguration is positioned as the pivotal bridge between corporate strategic coordination and competitiveness outcomes, with bidirectional arrows illustrating how performance feedback refines both AI models and organizational structures [13, 23]. Without complementary investments in organizational learning and governance, however, AI can inadvertently create capability rigidity—over-reliance on historical data patterns that lock firms into suboptimal trajectories [17, 21]. Thus, strategic renewal under AI mediation is conditional: it flourishes only when firms treat capability development as an ongoing, socio-technical process rather than a one-time technology implementation [18, 29].
As visualized in Figure 1 and formalized in Proposition 1, competitiveness in an AI-mediated economy arises from the systemic interplay of data infrastructures, intelligent decision systems, and organizational coordination. The central role of AI-mediated decision structures—depicted as the hub of the framework—links raw intelligence to strategic action, generating network effects and user value that traditional resource-based advantages cannot replicate [2, 13, 14]. Firms that achieve deep integration report not only higher agility but also novel forms of competitive advantage rooted in ecosystem orchestration and personalized value propositions [9, 25].
An extended analysis of the literature shows that competitiveness is further amplified when AI enables predictive market positioning and rapid iteration of corporate strategy [12, 26]. The feedback loops in Figure 1 ensure that competitiveness outcomes continuously recalibrate the entire architecture, creating a virtuous cycle of intelligence refinement and organizational redesign. However, this cycle is not automatic; it requires deliberate managerial attention to prevent superficial adoption that merely automates legacy processes without altering underlying strategic logic.
Proposition 2 illuminates how AI contracts traditional firm boundaries while expanding ecosystem participation. By lowering transaction costs and enabling seamless data exchange, AI facilitates more fluid make-or-buy decisions, open innovation platforms, and servitization strategies [15, 19, 27]. Corporate strategy, therefore, shifts from internal optimization to ecosystem orchestration, where competitive advantage derives as much from network positioning as from proprietary assets [2, 25].
This reconfiguration carries dual implications for diversification: AI-powered analytics enable finer-grained portfolio management, while ecosystem integration opens avenues for adjacent-market entry with reduced risk [20, 23]. Firms that master these boundary dynamics achieve higher resilience and innovation rates, yet those that neglect relational governance risk dependency on external AI platforms and potential loss of strategic control [7, 22].
In an AI-mediated economy, corporate strategy is fundamentally redefined as a continuous, intelligence-driven coordination process rather than a static planning exercise. Firm competitiveness no longer depends solely on AI adoption, but also on the depth of its integration into decision systems, governance structures, and capability reconfiguration. Organizations that successfully align AI with strategic intent, learning processes, and adaptive governance achieve sustained advantage through speed, flexibility, and ecosystem positioning, while those that fail to do so risk fragmentation and strategic erosion.
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Institute for Management, Business, and Accounting Studies
