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The Gartner Hype Cycle (GHC) remains a cornerstone of technology strategy; however, the rapid acceleration of Generative AI (GAI) challenges its predictive validity. This study evaluates the GHC’s utility through a systematic review of 41 Web of Science-indexed articles, classifying academic discourse into supportive, critical, and adaptive positions. By contrasting GHC stage assignments with empirical adoption signals, including user engagement, media sentiment, and investment trends, we identify a significant divergence, particularly regarding the “Trough of Disillusionment.” Our analysis demonstrates that this misalignment undermines the model’s strategic value in high-velocity contexts. Consequently, we propose a replicable, data-driven framework utilizing publicly available indicators to complement traditional GHC analysis, ensuring that decision science remains commensurate with the rapid evolution of intelligent systems.

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From Hype to Judgment: Reassessing the Gartner Cycle for Decision-Making in the Age of Generative AI

  • 会议论文

  • 发表日期：

    2026年

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  • Juan Pablo Mora-López,
  • David Lopez-Lopez,
  • Olga Rivera-Hernaez

摘要

The Gartner Hype Cycle (GHC) remains a cornerstone of technology strategy; however, the rapid acceleration of Generative AI (GAI) challenges its predictive validity. This study evaluates the GHC’s utility through a systematic review of 41 Web of Science-indexed articles, classifying academic discourse into supportive, critical, and adaptive positions. By contrasting GHC stage assignments with empirical adoption signals, including user engagement, media sentiment, and investment trends, we identify a significant divergence, particularly regarding the “Trough of Disillusionment.” Our analysis demonstrates that this misalignment undermines the model’s strategic value in high-velocity contexts. Consequently, we propose a replicable, data-driven framework utilizing publicly available indicators to complement traditional GHC analysis, ensuring that decision science remains commensurate with the rapid evolution of intelligent systems.