Compiled by Alex Gomez. Published October 5, 2026 · Last updated October 28, 2026.
AI resolution rate is the share of AI-handled conversations an AI agent finishes end to end, with no human completing the work and no repeat contact on the same issue within seven days. It is the headline number among AI agent metrics, and the easiest to inflate, because every vendor picks its own rules for both halves of the fraction.
TL;DR: Every AI resolution rate is a numerator and a denominator, and vendors choose both — Intercom's documentation shows the same 250 resolutions scoring 33% or 50% — so never compare two rates until you know what each one counts and divides by.
Methodology & sources
- Definitions read from the live pages of Zendesk (two guides, May and June 2026), Intercom Fin, Ada, Talkative and Aissist.io.
- Benchmarks from Fin, the My AskAI and Comm100 datasets (as reported by Lorikeet) and Gartner, each with its denominator and sample.
- Worked example numbers are illustrative. All figures verified 28 October 2026 (Gartner's 14% via Fin's and Talkative's citations; its newsroom blocks automated reads).
- Disclosure: this is Aissist's blog, and Aissist sells AI agents that are measured on this metric.
What is AI resolution rate, and how do you calculate it?
AI resolution rate equals conversations the AI resolved, divided by the conversations you count as eligible, times 100. Zendesk's automated resolution rate guide gives the textbook version: 150 issues fully resolved out of 200 AI-handled interactions is 75%. That formula is correct. It is also where the trouble starts, because both halves of the fraction are judgement calls.
Here is one month at a hypothetical support team (illustrative numbers):
| Step | Conversations |
|---|---|
| All inbound conversations | 10,000 |
| Routed to the AI agent | 7,500 |
| AI actually answered (others escalated by rule first) | 6,000 |
| Ended with no human handoff | 4,200 |
| …of which the customer abandoned without confirming | 600 |
| …of which the customer came back within 7 days | 300 |
| Verified resolutions (4,200 − 600 − 300) | 3,300 |
Now pick a fraction:
| Numerator ÷ Denominator | Resolution rate |
|---|---|
| Verified resolutions ÷ all inbound | 33% |
| Verified resolutions ÷ routed to AI | 44% |
| Verified resolutions ÷ AI answered | 55% |
| No handoff ÷ AI answered | 70% |
Same team, same month, same customers. The spread is 37 points, and nobody lied. The first row is what finance cares about; the last is containment dressed up as resolution. Most published numbers sit near the bottom, and almost none say which row they are.
What counts as "resolved"? Six published definitions compared
Vendors disagree on the numerator: some count only confirmed fixes, some let a model judge quality, some count silence, and some credit a good handover. Each choice moves the number in a predictable direction, and only two of the six below publish a re-contact window at all.
| Definition owner | What counts as resolved | Re-contact window | Escalated conversations | Source type |
|---|---|---|---|---|
| Aissist.io (this page) | Finished end to end, no human completing it; abandoned and assumed resolutions excluded | 7 days, any channel | Not resolved | Vendor standard |
| Zendesk | Accurate answer, required action completed, no follow-up; abandoned and partial answers excluded | 24–48 hours (May 2026 guide); none stated in the June 2026 guide | Not resolved | Vendor definition |
| Intercom Fin | Customer confirms the issue is solved, or does not follow up | None stated | Not resolved | Vendor definition |
| Ada | An LLM judges the conversation relevant, accurate, safe and contained | None; idle chats close after 24 hours, email after 72 | Marked Not Resolved at handoff | Vendor definition |
| Talkative | Confirmed, task completed, or no repeat contact in "a defined period" | Unspecified | Tracked separately as AI-assisted resolution | Vendor definition |
| Gartner | The customer says the issue was fully resolved in self-service | n/a | n/a | Independent survey |
Zendesk's own guide concedes the point:
"Before tracking the metric, define what a complete AI resolution means for your team." — Candace Marshall, VP Product Marketing, AI and Automation, Zendesk
We use seven days because a short window only catches fast failures. A refund promised in chat that never arrives shows up on day four, usually on another channel; a 24- or 48-hour window has already counted it as a win. Lorikeet's audit guide makes the same argument for a 7-day recontact check.
The label matters as much as the rules. Lorikeet's analysis of the My AskAI dataset found figures labelled "resolution" had a 72.5% median (108 deployments) versus 61% for "automation" (52 deployments) — an 11.5-point gap before any difference in capability, per Lorikeet's write-up.
Some vendors skip the word "resolution" altogether. Freshworks CEO Dennis Woodside reported deflection in August 2026:
"Freddy AI Agent deflection rates average 50%" — Dennis Woodside, CEO and President, Freshworks, via CX Today
Deflection counts every conversation that never reached a human, including customers who gave up, which is why Aissist's 2026 benchmark puts deflection 20–40 points above genuine resolution on the same deployment.
How do denominators change the number?
The denominator is the quieter lever: it can move a rate 17 points without one extra customer helped. Intercom proved it in July 2026, publicly and to its credit.
Intercom's update to Fin performance metrics, rolled out to existing workspaces July 1–8, 2026, stopped counting conversations where Fin was active but never got to answer. In Intercom's worked example, the resolution rate went from 250 ÷ 750 = 33% to 250 ÷ 500 = 50%, while the automation rate — resolutions over all conversations — stayed at 25%. Intercom states plainly: "The number of resolutions is not changing."
The new definition is arguably fairer. It is also a different number: any chart spanning July 2026 compares two metrics with the same name.
Three denominator questions settle most disputes:
- All inbound, or only AI-routed? Routing easy intents to AI lifts the rate without lifting capability.
- Are pre-answer escalations excluded? Removing them helps the ratio.
- Conversations or messages? Aissist's reliability benchmark uses conversations, because a message-level denominator inflates volume and flatters the rate.
A rate is a fraction. Read the bottom half first.
What is a good AI resolution rate?
A mature AI agent typically resolves 56–80% of the conversations it handles, with a 70% median across 195 published deployments — but only figures with a stated denominator can be compared. Here is every benchmark on this page, with what each one divides by.
| Source (read Oct 2026) | Figure | Numerator | Denominator / sample | Provenance |
|---|---|---|---|---|
| My AskAI dataset via Lorikeet, May 2026 | 70% median, middle half 56–80% | Mixed vendor definitions | 195 deployments | Independent compilation |
| Fin | 76% average | Confirmed or no follow-up | Conversations Fin handled, 12,000+ customers | Vendor-claimed |
| Fin benchmarks, refreshed May 2026 | 85% resolution, 78% automation (top 10 per industry) | Confirmed or no follow-up | 110M+ conversations, last three months | Vendor-claimed |
| Comm100, via Lorikeet | 44.8% | AI resolution, unfiltered | 220M+ live chat interactions | Independently reported |
| Aissist.io | 83% average, up to 98% in mature deployments | No human, no repeat contact within 7 days | AI-handled conversations; sample size not published | Vendor-claimed |
| Gartner, Dec 2023 survey | 14% fully resolved in self-service | Customer-reported | 5,728 customers, all self-service | Independent survey |
The spread from 14% to 85% is mostly definitions, not quality; for the industry-by-industry view, see average AI resolution rate in 2026. Gartner's row measures something else entirely — the customer's view of all self-service — which Fin's own guide contrasts with the 65% self-service resolution vendors report.
We hold our own row to the same standard. Aissist publishes the definition, the window and the denominator type, not yet a conversation count — the gap this article asks every vendor to close.
Which metrics should sit next to resolution rate?
Pair resolution rate with CSAT on AI-handled conversations and a re-contact rate, because a resolution rate alone can be raised by closing tickets nobody solved.
| Metric | What it measures | How it gets gamed | What catches it |
|---|---|---|---|
| Containment rate | Conversations that never reached a human | Hide the "talk to a human" button | Re-contact rate, CSAT |
| First contact resolution | Issues solved with no repeat contact within 7 days | Shorten the window | Fixed, published window |
| Average handle time | Minutes per conversation | Close conversations early | Re-contact rate |
| Cost per resolution | Total cost ÷ resolved issues | Count assumed resolutions | Verified numerator |
| Re-contact rate | Share of "resolved" issues that return | Treat a new channel as a new issue | Customer-level matching |
| CSAT on AI conversations | Satisfaction where AI handled it | Survey only resolved chats | Survey all AI conversations |
Aissist's 2026 benchmark reports AI-handled CSAT typically running 5–10 points below the same team's human-handled score, and its resolution–CSAT analysis argues satisfaction tends to fall once teams push automation past the work the AI can actually finish. Resolution without CSAT is a speedometer without a fuel gauge.
"If you invest in understanding, adoption, and great content, AI performance takes off." — Yamine Gluchow, VP of Information Systems, Lightspeed, via Fin
Resolution rate is a ratio — read the bottom half first
AI resolution rate is the right headline metric for an AI agent, and the easiest to flatter. Whether you are setting a target or checking a vendor's claim, hold the number to five lines — the Aissist resolution-rate standard:
- Numerator: issues finished end to end, with no human completing them.
- Exclusions: abandoned and assumed resolutions removed.
- Re-contact window: seven days, on any channel, fixed.
- Denominator: all AI-handled conversations, with the all-inbound rate beside it.
- Sample: conversation count and date window.
A vendor that fills in all five has a number worth comparing. To see where your own rate lands, try Aissist's resolution rate benchmark tool; for the billing side, read deflection vs resolution rate.
Frequently asked questions
What is the formula for AI resolution rate?
AI resolution rate = conversations the AI resolved end to end ÷ eligible conversations × 100. Always state what counts as resolved, the re-contact window, and which conversations are eligible, because all three change the result.
What is the difference between containment rate and resolution rate?
Containment rate counts conversations that never reached a human, including customers who gave up. Resolution rate counts only conversations where the issue was actually solved, so on the same data it reads lower. See containment rate for how to audit a containment claim.
Is first contact resolution the same as AI resolution rate?
No. First contact resolution measures whether an issue was solved without a repeat contact inside a set window, across human and AI channels. AI resolution rate measures only conversations the AI handled, and some vendors never check for repeat contact at all.
How long should the re-contact window be?
Seven days, on any channel, is the window Aissist uses across its guides, because it catches slower failures such as a refund that never arrives. Zendesk's May 2026 guide uses 24–48 hours; Fin and Talkative state no fixed window.
How fast does AI resolution rate improve after launch?
Aissist's 2026 benchmark reports new deployments typically launching at 40–50% and improving around one point per month, as knowledge content and actions are added.
Changelog
- 28 October 2026 — Re-contact window set to seven days to match Aissist's other guides; Zendesk, Fin, Ada and Talkative definitions compared side by side; all figures re-verified.
- 5 October 2026 — First published.
