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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 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.

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.