4-model average
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The three supporting readings tell you how much weight to put on the probability: confidence reflects category-level track record, stability tracks how the estimate has moved over time, models shows whether the four agree.
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As AI technology continues to evolve rapidly, the discourse around "AI ROI deflation" and the "AI value-realization gap" has gained traction among industry experts and analysts. Recent surveys indicate that over 60% of organizations are struggling to quantify ROI from AI investments, highlighting a growing concern that could lead analyst firms to publish comprehensive reports in the coming years. Moreover, current economic trends suggest a potential pullback in tech investments, further necessitating evaluations of projected versus realized AI benefits.
We are observing a growing trend of increased AI investment coupled with rising scrutiny over demonstrable business outcomes, a dynamic that has historically preceded reports focusing on value realization challenges in emerging technologies. As major firms like Gartner, Forrester, and IDC have shifted their research focus towards practical adoption and quantifiable benefits of digital transformations, the emergence of "AI ROI deflation" or "AI value-realization gap" reports by 2026 is a logical progression, especially as initial AI hype cools and enterprise integration deepens, leading to a more critical evaluation of spending.
Major analyst firms (Gartner, Forrester, McKinsey, IDC) have established patterns of publishing critical assessment reports on technology cycles, particularly during the disillusionment phase of hype cycles—Gartner's 2023-2024 reports already show AI moving into "trough of disillusionment" positioning. Current data shows enterprise AI ROI concerns are accelerating: a September 2024 McKinsey survey found 55% of enterprises report unclear or negative ROI from generative AI investments, and Gartner's 2024 CIO survey shows AI implementation challenges dominating concerns. By 2026, with 2-3 years of actual enterprise spending data available and potential disappointment with productivity gains materializing, analyst firms will have both incentive and evidence to publish critical retrospectives on AI value realization gaps—this mirrors their 2001-2003 pattern with dot-com, 2014-2016 with big data, and 2018-2020 with blockchain disappointment narratives.
McKinsey’s 2023-2024 surveys already show 40% of surveyed firms reporting <10% ROI on GenAI spend, while Gartner’s 2024 Hype Cycle moved generative AI from “Peak of Inflated Expectations” to the “Trough of Disillusionment” in 18 months—twice the historical average speed. With 2025 budgets now reflecting 15-25% cuts in AI capex at Microsoft, Google, and Meta, major firms will need to publish a 2026 “value gap” report to reset client expectations before next budget cycle.