Why One in Five Organizations Will Abandon AI by 2028

These findings align with Gartner's separate April 2026 estimate that 57% of IT infrastructure and operations managers have at least one AI project failure behind them, and that one in five AI projects in that domain collapses entirely .

The Spending-ROI Disconnect: Evidence from Multiple Sources

Multiple major sources corroborate the growing disconnect between massive AI investment and measurable business outcomes:

Goldman Sachs (August 2026): Strategist Ben Snider reports that AI spending is surging sharply, but the technology has yet to produce a meaningful improvement in earnings for most companies. Only 2% of S&P 500 companies have quantified AI's impact on their earnings, and most report little to no immediate benefit . Analyst Jessica Rindels estimates U.S. AI investment will total almost $600 billion in 2026, equivalent to nearly 2% of GDP, yet evidence of matching economic gains remains thin .

Goldman Sachs (May 2026): Analyst Jim Covello notes that most enterprises have yet to generate any returns from their AI spending, citing an MIT study that found 95% of enterprise AI pilots delivered no measurable P&L impact . Covello argues the economics of AI are "more questionable today than two years ago" .

Goldman Sachs (July 2026): Economist Jan Hatzius and analyst David Peng offer a reality check — AI adoption and its productivity payoff may take far longer than markets expect. Their analysis shows productivity impact follows a J-curve: a modest drag for the first four years, statistically significant gains only after eight years, and a peak impact of roughly 0.6 percentage points in year 12. If ChatGPT's 2022 launch is the equivalent of the PC's 1981 debut, the payoff is still years away .

McKinsey (2026): Fewer than 20% of enterprise AI deployments show measurable business impact .

Sequoia Capital: Has flagged a roughly $600 billion revenue gap between AI investment and AI return .

Hyperscaler cash flow risk: Goldman Sachs warns that the four dominant cloud vendors (Amazon, Alphabet/Google, Microsoft, Meta) are collectively on course to spend more than their entire operating cash flow on AI infrastructure, with no proven enterprise ROI to justify the outlay . Combined 2026 capex for these four companies is projected at roughly $650 billion .

Why Projects Fail: It's Not the Technology

A consistent finding across all major analyses is that AI project failures are organizational and strategic, not technical. The model almost never fails — but the business case, data foundation, and cost controls do.

Gartner identifies three primary failure modes driving its predictions: escalating costs with no clear ROI model, unclear business value, and inadequate risk controls . As one Gartner analyst put it, "most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and often misapplied" .

The consequences are already visible. Gartner predicts that by 2028, 70% of AI initiatives will be decommissioned due to unmanaged cost explosions and value drift in organizations lacking a dedicated AI financial management practice .

What This Means for Business Leaders

The pattern across Gartner, Goldman Sachs, McKinsey, MIT, and Sequoia is consistent: AI spending is at historic highs, but the vast majority of organizations cannot yet demonstrate measurable, bottom-line returns from that spending. Many are already pulling back or failing to get projects past the pilot stage.

For organizations investing in AI, the research points to a clear path forward: identify a specific operational bottleneck with measurable cost or quality impact, build an AI solution for that specific problem, measure the before/after impact rigorously, and scale only what works . Projects that fail to establish clear ROI metrics from day one are unlikely to survive the coming reckoning.