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The rapid integration of algorithmic systems into organizational decision-making has transformed how managers exercise judgment in digitally transformed firms. This managerial and strategic perspective article explores the implications of data-driven decision processes, where algorithms increasingly inform rather than supplant human insight. Synthesizing published studies, the analysis focuses on the interaction between human judgment and algorithmic recommendations, managerial reliance on predictive analytics, and the resulting need for organizational redesign and governance. Key strategic challenges include the risk of over-reliance on algorithmic outputs, the potential erosion of managerial autonomy, and the complexities of human-AI collaboration. Drawing on leading journals in strategic management and information systems, the article argues that while algorithmic systems enhance decision speed and accuracy, they also introduce governance dilemmas and require new accountability structures. In digitally transformed environments, firms must address how data-driven processes reshape managerial roles and strategic authority. The paper identifies organizational consequences, including shifts in power dynamics and the need for adaptive learning mechanisms. By examining these elements, it lays the foundation for a managerial framework that balances algorithmic efficiency with human strategic judgment, highlighting risks like bias and opportunities for enhanced competitive positioning. Effective governance of algorithm-supported decisions is essential for sustainable digital transformation. This perspective contributes to understanding how organizations can thrive when algorithms inform managerial judgment without diminishing the human element critical to strategic success.

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 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.