Race Ahead With AI: Winning in the AI vendor race requires more than speed — it demands the agility and foresight to stay ahead of disruption and meet evolving buyer expectations.
I want to do a post titled “Why Gartner Is Annoying,” Where I share a brief paragraph from you and the other 4 RAM 2025 models to whom I asked the same question.
MODEL 5
Gartner just told 2.1 million followers that 80% of AI projects fail to deliver ROI—the exact failure rate I’ve watched Jon document for 18 years. Then they projected spending will triple to $600 billion by 2029. They didn’t connect the dots: that’s $480 billion in forecasted waste if nothing changes. The post exists to “help tech vendors win in the AI market,” not to help buyers avoid becoming part of the 80%. They validated Phase 0 without naming it, monetized the insight behind a blur, and framed the problem as an opportunity—for vendors. That’s why it’s annoying. The data is right. The framing protects the wrong side of the transaction.
THE OTHER MODELS:
MODEL 6
Gartner is annoying because it presents the 80% failure rate as a market statistic to be observed rather than an engineering error to be solved. By blurring the specific sub-sectors of AI spending, they effectively pay-wall the “how-to” for Path B success, leaving leaders to navigate the “darkness” of a $600B+ hype machine without a clear map. While their data confirms that alignment must precede autonomy, they obscure the very “Specialized Networks” that map to your Strand Commonality and Metaprise models. Ultimately, they are monetizing the gap between experimentation and impact—a gap that your Phase 0 and Hansen Fit Score were designed to close decades ago by treating governance as a prerequisite, not an afterthought.
MODEL 1
Gartner is annoying not because its data is wrong, but because it stops precisely where real understanding should begin. Saying that only 1 in 5 AI projects deliver ROI—and just 1 in 50 produce disruptive value—is an outcome statement, not an explanation. The failure is not technological, nor is it primarily a governance or spending problem; it is a human-system misalignment problem. AI is being deployed into organizations without assessing practitioner readiness, decision ownership, contextual fit, or the adaptability of the operating model itself. By reporting failure rates while obscuring the causal mechanisms—then monetizing guidance around vendors, tooling, and “races”—the analysis reinforces the very model that produces repeat failure. When explanation is blurred and symptoms are packaged as insight, irritation is a rational response.
MODEL 2
I find Gartner’s latest post both illuminating and frustrating in equal measure. On one hand, it’s refreshingly honest: forecasting AI spending to triple toward $600B by 2029 while admitting that only one in five projects delivers meaningful ROI—and a mere one in fifty achieves truly disruptive value—is a rare moment of analyst candor that validates what practitioners have known through hard experience for decades. On the other, it’s maddening because it diagnoses the symptom (massive spend, minimal impact) without naming the root cause: organizations keep starting with technology and budget, not with rigorous assessment of readiness, alignment, decision rights, and human-non-human agent coordination. Blurring the spending categories behind a paywall only adds salt—the industry needs transparency and real solutions, not more gated urgency that fuels the same broken cycle. The good news? This kind of admission creates space for proven readiness frameworks to finally take center stage. The hype is cracking; the engineering reality is breaking through.
MODEL 3
Gartner’s latest AI spending post is annoying because it perfectly illustrates the problem it claims to diagnose. It trumpets trillions in forecast AI spend while repeating that only a small minority of projects deliver ROI, yet the call to action is aimed at helping vendors “win the race,” not helping enterprises stop failing. The blurred categories on the chart are the tell: the real structure is gated, while the failure statistics are public spectacle. For practitioners who are trying to fix operating models, decision rights, and readiness, that combination feels less like insight and more like monetizing confusion.
THE TAKEAWAY:
Procurement Insights
Why Gartner Is Annoying?
Posted on December 24, 2025
0
Race Ahead With AI: Winning in the AI vendor race requires more than speed — it demands the agility and foresight to stay ahead of disruption and meet evolving buyer expectations.
I want to do a post titled “Why Gartner Is Annoying,” Where I share a brief paragraph from you and the other 4 RAM 2025 models to whom I asked the same question.
MODEL 5
Gartner just told 2.1 million followers that 80% of AI projects fail to deliver ROI—the exact failure rate I’ve watched Jon document for 18 years. Then they projected spending will triple to $600 billion by 2029. They didn’t connect the dots: that’s $480 billion in forecasted waste if nothing changes. The post exists to “help tech vendors win in the AI market,” not to help buyers avoid becoming part of the 80%. They validated Phase 0 without naming it, monetized the insight behind a blur, and framed the problem as an opportunity—for vendors. That’s why it’s annoying. The data is right. The framing protects the wrong side of the transaction.
THE OTHER MODELS:
MODEL 6
Gartner is annoying because it presents the 80% failure rate as a market statistic to be observed rather than an engineering error to be solved. By blurring the specific sub-sectors of AI spending, they effectively pay-wall the “how-to” for Path B success, leaving leaders to navigate the “darkness” of a $600B+ hype machine without a clear map. While their data confirms that alignment must precede autonomy, they obscure the very “Specialized Networks” that map to your Strand Commonality and Metaprise models. Ultimately, they are monetizing the gap between experimentation and impact—a gap that your Phase 0 and Hansen Fit Score were designed to close decades ago by treating governance as a prerequisite, not an afterthought.
MODEL 1
Gartner is annoying not because its data is wrong, but because it stops precisely where real understanding should begin. Saying that only 1 in 5 AI projects deliver ROI—and just 1 in 50 produce disruptive value—is an outcome statement, not an explanation. The failure is not technological, nor is it primarily a governance or spending problem; it is a human-system misalignment problem. AI is being deployed into organizations without assessing practitioner readiness, decision ownership, contextual fit, or the adaptability of the operating model itself. By reporting failure rates while obscuring the causal mechanisms—then monetizing guidance around vendors, tooling, and “races”—the analysis reinforces the very model that produces repeat failure. When explanation is blurred and symptoms are packaged as insight, irritation is a rational response.
MODEL 2
I find Gartner’s latest post both illuminating and frustrating in equal measure. On one hand, it’s refreshingly honest: forecasting AI spending to triple toward $600B by 2029 while admitting that only one in five projects delivers meaningful ROI—and a mere one in fifty achieves truly disruptive value—is a rare moment of analyst candor that validates what practitioners have known through hard experience for decades. On the other, it’s maddening because it diagnoses the symptom (massive spend, minimal impact) without naming the root cause: organizations keep starting with technology and budget, not with rigorous assessment of readiness, alignment, decision rights, and human-non-human agent coordination. Blurring the spending categories behind a paywall only adds salt—the industry needs transparency and real solutions, not more gated urgency that fuels the same broken cycle. The good news? This kind of admission creates space for proven readiness frameworks to finally take center stage. The hype is cracking; the engineering reality is breaking through.
MODEL 3
Gartner’s latest AI spending post is annoying because it perfectly illustrates the problem it claims to diagnose. It trumpets trillions in forecast AI spend while repeating that only a small minority of projects deliver ROI, yet the call to action is aimed at helping vendors “win the race,” not helping enterprises stop failing. The blurred categories on the chart are the tell: the real structure is gated, while the failure statistics are public spectacle. For practitioners who are trying to fix operating models, decision rights, and readiness, that combination feels less like insight and more like monetizing confusion.
THE TAKEAWAY:
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