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The rapid integration of artificial intelligence (AI) and algorithmic systems into organizational decision processes has transformed how strategic choices are made. Machine-generated insights provide data-driven predictions, pattern recognition, and scenario analyses that augment human managerial judgment. Yet, they also introduce tensions such as over-reliance, algorithmic bias, and reduced interpretive flexibility. This theory-development article synthesizes the literature on human-AI collaboration in strategic contexts to propose a conceptual model that explains the dynamic interplay between algorithmic outputs and human cognition. Drawing on the automation-augmentation paradox and related frameworks, we highlight complementarities—where algorithms enhance speed and objectivity—and tensions—where human intuition contextualizes uncertainty and ethical considerations. We develop propositions addressing algorithmic influence on strategic interpretation, managerial cognition under data-driven conditions, organizational factors moderating reliance on insights, and governance mechanisms for accountable AI-informed choices. This work advances understanding of hybrid decision systems in digital organizations, offering implications for balancing augmentation with human oversight to foster effective strategic outcomes.