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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 maturation of artificial intelligence and automation technologies is driving a fundamental shift in digital firms, where business processes evolve from human-managed or rule-based automated operations to fully autonomous systems capable of self-execution, adaptation, and decision-making. This theory-development article conceptualizes the emergence of autonomous business processes and delineates their transformative implications for organizational design. Integrating streams of research on robotic process automation, algorithmic management, digital transformation, and human–AI collaboration, we advance a novel theoretical framework that explains the mechanisms of transition and the resulting reconfiguration of structures, authority distribution, and governance. We argue that increasing automation sophistication diminishes traditional hierarchical controls, fosters hybrid human-machine ecosystems, and demands new accountability frameworks to sustain strategic oversight. Six propositions articulate the causal pathways linking AI-enabled execution, process autonomy, decision authority redistribution, and organizational redesign. A conceptual model visually maps the progression from human-controlled to autonomous layers, incorporating feedback loops for continuous learning. Contributions to digital business and management studies include a foundational theory for navigating automation-induced transformations, with implications for leaders seeking to balance machine autonomy with human strategic input while mitigating risks of opacity and power imbalances. This framework provides a platform for future inquiry into the performance and ethical outcomes of autonomous processes in digital organizations.

The integration of artificial intelligence (AI) into strategic management has transformed how organizations design decision systems and pursue competitive advantage in digital environments. This narrative literature review synthesizes peer-reviewed studies, focusing on AI-enabled strategic decision-making, algorithmic organizational systems, and the mechanisms through which AI generates sustained performance gains. Drawing on top-tier journals such as Strategic Management Journal, MIS Quarterly, and Technological Forecasting and Social Change, the analysis identifies recurring patterns of augmentation rather than replacement. It surfaces persistent tensions in human–AI collaboration and governance. Key findings show that AI augments predictive accuracy and resource allocation, yet introduces novel risks related to algorithmic bias, ethical oversight, and the erosion of traditional sources of advantage. The review traces the field’s evolution from early conceptual explorations of human–AI symbiosis to more recent examinations of generative AI’s disruptive potential and firm-level outcomes. Conceptual overlaps emerge around the centrality of hybrid decision architectures, while inconsistencies appear in assessments of long-term competitive sustainability. By mapping these streams and their interrelationships, the manuscript offers a structured foundation for understanding AI’s strategic role. It highlights critical gaps in cross-industry generalizability, ethical frameworks, and the interplay between technological affordances and organizational adaptation. This synthesis equips scholars and executives with an integrated lens on how AI is reconfiguring strategic management in the digital age.

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.

Generative artificial intelligence has emerged as a transformative force in business and management, fundamentally altering how organizations create value, make decisions, and manage knowledge. This narrative review synthesizes contemporary scholarship to examine how generative AI functions as a new layer of organizational capability, creating strategic opportunities while simultaneously introducing significant managerial, ethical, and governance challenges. The analysis reveals that generative AI differs fundamentally from prior forms of artificial intelligence through its capacity for content generation, contextual reasoning, and human-like interaction, enabling unprecedented applications in innovation management, strategic decision support, knowledge work transformation, and business model experimentation. However, these capabilities generate corresponding vulnerabilities, including algorithmic hallucination, embedded bias, operational opacity, and organizational over-dependence. The review identifies three major tensions: augmentation versus automation, efficiency versus reliability, and innovation acceleration versus governance lag. Drawing on scholarly sources, this article proposes an integrative framework situating governance and human oversight as essential mediating mechanisms between generative AI capabilities and organizational outcomes. The findings suggest that successful generative AI adoption requires organizations to balance opportunity exploitation with risk mitigation through structured accountability systems, human-in-the-loop protocols, and adaptive governance architectures.

Artificial intelligence is becoming increasingly embedded in business management, influencing decisions in strategy, operations, marketing, human resources, finance, and organizational control. Its managerial significance no longer lies only in its capacity to process information faster than humans, but in its growing ability to recommend, rank, predict, allocate, and sometimes decide. This shift raises important questions about how organizations should govern AI when it begins to affect managerial judgment itself. The central problem addressed in this review is that management research has often treated AI as a performance-enhancing tool while giving less sustained attention to its governance consequences. Three tensions remain particularly fragmented: the delegation of decision authority to algorithmic systems, the maintenance of organizational accountability in distributed human-machine arrangements, and the conditions under which managers trust or distrust AI-assisted decisions. These issues are analytically distinct but practically interdependent. The objective of this critical review is to synthesize literature on AI in business management through the integrated lenses of authority, accountability, and trust. Rather than presenting AI adoption as an inevitable route to efficiency, the review interrogates the organizational assumptions behind AI-enabled decision-making. It asks how AI changes managerial discretion, responsibility, oversight, and confidence in organizational decisions. The review concludes that AI governance in management must move beyond technical performance and address the institutional conditions under which AI-assisted decisions are authorized, explained, contested, and trusted. Authority, accountability, and trust should not be treated as separate implementation concerns but as a connected governance triad. Future management research should therefore conceptualize AI not merely as a tool, but as a socio-technical actor that reshapes managerial responsibility and organizational control.

Organizations are investing in artificial intelligence with expectations of improved efficiency, stronger decision quality, better customer experiences, and new sources of competitive advantage. Yet many AI initiatives do not progress beyond experimentation or fail to produce durable business value after deployment. A central reason is that firms often treat AI deployment as a technical implementation challenge rather than as an organizational readiness problem. Existing readiness models help organizations evaluate digital capability, while business value frameworks help managers estimate the strategic and financial promise of technology investments. However, these two assessments are frequently conducted separately. As a result, decision-makers may pursue AI initiatives that appear strategically attractive but exceed the organization’s practical capacity to implement, govern, and absorb them. This article develops the AI Readiness–Business Value Matrix as an original conceptual model for assessing organizational preparedness before artificial intelligence deployment. The matrix maps organizational readiness against expected business value potential, enabling firms to classify AI initiatives into practical decision categories. It is designed to support managerial judgment before major resources are committed. The article synthesises peer-reviewed publications published across AI adoption, organizational readiness, digital transformation, business value, and strategic management. The resulting model defines readiness dimensions, value categories, and four strategic quadrants that guide whether organizations should proceed, prepare, pivot, or postpone AI deployment. Four tables operationalise the model by detailing preparedness dimensions, business value potential, matrix design, and application scenarios. The AI Readiness–Business Value Matrix contributes a practical diagnostic tool for aligning AI ambition with organizational preparedness. It encourages organizations to move beyond technology enthusiasm toward structured evaluation of capability, governance, culture, and strategic value. By doing so, it can reduce deployment failure risk and improve the allocation of scarce managerial, technical, and financial resources.