Updated on October 14, 2026
Comm100’s 2026 benchmark analysis puts a good AI resolution rate at 65% to 80%, depending on how well the AI is tuned to your own documentation. The all-industry average sits well below that. Where you land depends on four things: how “resolved” is defined, your ticket types, your channel mix, and the quality of your knowledge and integrations.
Why published AI resolution rates disagree
Published resolution rates vary widely, and much of that spread is definitional. The same conversation can count as resolved under one platform’s rules and not under another’s, depending on whether inactivity counts, whether dissatisfied customers are excluded, which ticket types are measured, and what the denominator is.
Five terms get used interchangeably, and they measure different things:
| Term | Counts as a success when | What it can hide |
|---|---|---|
| Deflection | The conversation never reached a human agent | Customers who gave up, got a vague answer, or came back through another channel |
| Containment | The conversation stayed in the AI channel without escalating | Silence is logged as success |
| Resolution rate | The customer’s issue was solved end to end | Depends on the denominator and the resolution rule |
| Verified resolution | Resolved, no human intervention, and no recontact on the same issue within a fixed window | Requires recontact tracking |
| Automation rate | AI-resolved conversations as a share of all inbound volume | Blends how often the AI is involved with how often it resolves |
Your question, “what percentage of tickets,” is an automation rate question. Automation rate equals involvement rate times resolution rate, but most benchmarks report resolution rate, measured only on the conversations the AI handled. The gap can be large. In Comm100’s 2026 live chat benchmark of more than 220 million interactions, AI handled 75.3% of chats, and it resolved 44.8% of those. That works out to roughly 34% of all chats (our arithmetic, not a figure Comm100 reports).
Over-claiming has a cost beyond a flattering dashboard. A conversation that is closed but not solved comes back as recontact, tends to reach a human agent already frustrated, and drags CSAT down on both interactions. Treat any figure that arrives without a stated definition and denominator as marketing.
AI resolution rate benchmarks by team size
In the same dataset, resolution falls as teams route more volume to AI:
| Team size | AI resolution rate (share of AI-handled chats) |
|---|---|
| 1 to 5 agents | 89.0% |
| 6 to 10 agents | 69.6% |
| 11 to 25 agents | 47.8% |
| 26 or more agents | 41.2% |
The pattern follows the contact mix. The first slice of coverage is documented FAQ material, where the answer either exists or it does not. Pushing coverage higher pulls in account-specific, multi-step and judgment-heavy requests. A lower rate on a larger share of volume can remove more work than a high rate on a narrow slice, which is why automation rate, not resolution rate alone, is the number to watch. Set expectations during procurement, before the pressure to justify the spend arrives.
AI resolution rate benchmarks by industry
Industry shifts the ceiling because “resolved” means more in some sectors than others. The same benchmark reports these sector figures:
| Industry | AI resolution rate (share of AI-handled chats) |
|---|---|
| Non-profit | 97.7% |
| Education | 75.9% |
| Banking and finance | 75.2% |
| Health and pharma | 45.8% |
| iGaming | 38.1% |
The analysis attributes the low end to scale and to regulated decisions, such as compliance holds, verification disputes and self-exclusion requests, which need a named human accountable for them. That caps how much a regulated team can automate. Clean escalation with full context lets you automate what you can inside that limit.
AI resolution rate by channel
Few public benchmarks split AI resolution by channel, so treat this table as directional and measure your own.
| Channel | Why AI tends to do better or worse here |
|---|---|
| Chat | Real-time clarification, narrow intent, and usually the strongest channel |
| Several issues per message, asynchronous multi-turn resolution, harder intent classification on long unstructured text | |
| Social | Short, emotionally charged and brand-sensitive, with public visibility raising the cost of a wrong answer |
| Voice | Depends on authentication, speech recognition and whether the AI can complete backend actions |
Your overall rate is a volume-weighted average of every channel, so channel mix matters as much as the AI. Take two teams running the same AI with identical per-channel resolution (hypothetical figures: chat 70%, email 45%, voice 35%), assuming the AI handles all volume in each channel:
| Team | Chat share | Email share | Voice share | Blended rate |
|---|---|---|---|---|
| A | 60% | 25% | 15% | 58.5% |
| B | 30% | 35% | 35% | 49.0% |
Same AI, almost ten points apart. That is why comparing your rate with a competitor’s published number rarely tells you anything. The most credible claim reads “AI resolves X% of chat, Y% of email and Z% of voice contacts, under a stated resolution definition.”
What decides how high your rate can go
Knowledge quality. An AI agent can only resolve what its knowledge covers, so gaps in documentation become gaps in resolution. The benchmark analysis ties a good rate to how finely the AI is tuned to your internal documents. If your articles are incomplete, contradictory or written for internal use, the agent will either escalate unnecessarily or guess. Our guide to knowledge bases covers how to structure content for retrieval.
Action capability. Backend integrations separate answering from resolving. An agent that can only retrieve information caps out earlier than one that can verify identity, check live order status or issue a refund. Each integration you add raises the ceiling for that ticket category.
| Ticket type | Directional ceiling | Why |
|---|---|---|
| Password reset and account access | High | Identity check and reset steps can be fully automated |
| Order status | High | A lookup once the agent has live order-system access |
| Returns and standard refunds | Medium to high | Policy rules and refund limits can be encoded |
| Billing disputes | Lower | Usually need human authorization |
| Fraud flags and policy renewals | Lower | Compliance review and required escalation paths |
Directional, based on how much of each workflow can finish without a human decision.
Ticket complexity. Complexity sets the ceiling, and knowledge and integrations decide how close you get to it. Simple, well-defined requests sit near the top. Multi-step or judgment-heavy ones stay lower on every platform.
The model. Model quality matters, but it is one variable among several. Knowledge, integrations and escalation design shape the outcome just as much.
Behavior at the edge of confidence. What an AI agent does when it is unsure decides what gets counted as a resolution. An agent built to sound confident when it should not will report higher containment and more false resolutions. One that routes what it cannot answer to a person tends to report a lower headline number and a higher verified one. Kommunicate’s AI agents answer from your own knowledge sources and route unknown queries to human agents, and the platform can assign the conversation to a human agent when the AI agent cannot answer. Handoff quality matters to customers too. Kommunicate’s guide to human handoff treats sharing the full transcript with the agent as part of a well-designed handoff, and the benchmark reports 92.6% CSAT on handoffs from AI to a human agent, though that is a vendor benchmark.
How to measure real resolution
Verified resolution means the issue was completed, no human intervention was needed, and the customer did not return about the same issue within a fixed window. Pick one window (24, 48 or 72 hours) and keep it constant so your trend line stays comparable.
| Metric | How to calculate | What it tells you |
|---|---|---|
| Involvement rate | AI-handled conversations ÷ all inbound | How much volume the AI actually touches |
| Resolution rate | AI-resolved ÷ AI-handled | Performance on the work it was given |
| Automation rate | AI-resolved ÷ all inbound | Share of total volume closed without a human |
| Verified resolution rate | AI-resolved with no same-issue recontact in the window ÷ AI-handled | Resolution that holds up |
| False-resolution rate | AI-“resolved” conversations that were reopened, recontacted or failed QA review ÷ AI-“resolved” | How much of your headline number is not real |
| Recontact rate (24 and 72 hours) | AI-resolved customers who return on the same issue ÷ AI-resolved | The counter-metric to containment |
| CSAT, AI vs human | CSAT on AI-handled conversations compared with human-handled | Whether quality holds as automation grows |
| Answer accuracy | Share of sampled AI answers rated correct in QA review | Whether resolutions are actually right |
| Appropriate escalation rate | Escalations a reviewer judges necessary ÷ all escalations | Whether handoffs happen at the right moments |
| Cost per verified resolution | Total AI cost ÷ verified resolutions | The number finance should see |
Reporting deflection or containment alone gives leadership a misleading picture. High containment alongside rising recontact and falling CSAT is a warning sign, not a success.
How to raise your rate without hurting CSAT
- Start with high-frequency, low-complexity intents. Password resets, order status and account access have clear pass or fail outcomes. They build trust in the agent before you expand into billing or policy-sensitive topics, where a wrong answer costs more.
- Audit escalations before you tune the AI. When an agent hands off unnecessarily, the cause is usually documentation that is incomplete, contradictory or written for internal use. Rewrite your highest-traffic articles in a format an AI agent can retrieve accurately, then re-measure.
- Add one integration per ticket category, then measure again. Expand in steps so you can see which integration moved which category.
What the high end looks like
Conte.it, an Italian insurance company, automated 90% of queries about insurance purchases, renewals and refunds with Kommunicate. Read it as a result for three well-defined query types, and ask of it what you would ask of any vendor figure: what counts, over what window, on which tickets.
Analyst forecasts point the same way. Gartner predicts that by 2029 agentic AI will autonomously resolve 80% of common customer service issues without human intervention. That is a forecast about common issues, not every ticket.
The teams at the high end are not describing the same results with a bigger number. They scope tickets carefully, connect the systems that let the agent act, and hold themselves to a stricter definition of resolved.
To benchmark your own channel mix and ticket types, book a demo with the Kommunicate team. For the wider picture, see our guide to AI customer service, or the ecommerce customer service article if order status and returns dominate your queue.
FAQ
A good rate is one you can verify. Judge it against your ticket mix, channel mix and recontact rate, not a single headline number. Early deployments sit lower, and mature ones with strong knowledge and backend integrations sit higher. Compare yourself with your own baseline before comparing with a vendor’s published figure.
Deflection counts conversations that never reached a human, including customers who gave up. Resolution counts conversations where the customer’s problem was actually solved. A deflected conversation can return as recontact through another channel, so deflection can look healthy on a dashboard while resolution and CSAT tell a different story.
Automation rate is the share of all inbound conversations the AI resolves without a human. It equals involvement rate (AI-handled divided by all inbound) multiplied by resolution rate (AI-resolved divided by AI-handled). Use it when you want to know how much of your total volume the AI actually closes.
There is no industry-standard definition of “resolved.” Platforms differ on whether inactivity counts as resolution, whether dissatisfied customers are excluded, which ticket types are measured and what the denominator is. Ask each vendor for its definition, its recontact rate, and whether you can audit the conversations behind the number.
Take the conversations the AI marked as resolved and count how many were reopened, produced a recontact on the same issue within your window, or failed a QA review of the answer. Divide that by all AI-marked resolutions. A rising false-resolution rate means your headline number is overstating real performance.
Devashish Mamgain is the CEO & Co-Founder of Kommunicate, with 15+ years of experience in building exceptional AI and chat-based products. He believes the future is human and bot working together and complementing each other.
