The Strategic Impact of Generative Artificial Intelligence on Organizational Decision Making

Abstract

Generative Artificial Intelligence (AI) has emerged as a transformative technology with profound implications for organizational decision-making processes. Unlike traditional AI systems, generative AI can autonomously produce novel content, including text, images, models, and scenarios, enabling organizations to analyze complex data, forecast trends, and simulate strategic alternatives. Its application spans business intelligence, strategic planning, risk assessment, marketing, and innovation management. While generative AI offers substantial benefits for improving decision quality, speed, and efficiency, it also raises challenges related to trust, interpretability, and ethical deployment within corporate environments. Organizational decision-making is increasingly data-driven, and generative AI systems facilitate the synthesis of structured and unstructured information from diverse sources. These systems enable managers to explore multiple decision pathways, identify potential risks, and optimize strategic choices. Furthermore, generative AI can augment human creativity by producing innovative solutions and scenario planning alternatives that may not emerge through conventional analytical approaches. However, reliance on automated content generation also introduces risks of cognitive overreliance, algorithmic bias, and strategic misalignment. This study examines the strategic impact of generative AI on organizational decision-making by developing a conceptual framework that investigates the relationships between generative AI adoption, decision quality, strategic agility, and organizational performance. Empirical data were collected from senior managers, decision-makers, and AI adoption specialists across multiple industries. Structural Equation Modeling using Smart Partial Least Squares was applied to assess the relationships between constructs. The results indicate that generative AI adoption significantly enhances decision quality and strategic agility, which in turn positively influence organizational performance. Moreover, organizational culture and technological readiness moderate the effectiveness of generative AI integration in decision-making processes. This study contributes to literature on AI-driven strategic management by providing empirical evidence of generative AI’s role in shaping organizational decision-making outcomes and offering actionable insights for successful AI integration in corporate strategy.