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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In an October 6, 2026 snapshot of 13 AI support vendors, the top five averaged 70% automation, the median vendor scored 48%, and the conversation-volume-weighted rate was 46%. Those are results from a four-week test of ecommerce support on live stores—not a universal prediction for your support team. The later benchmark page from the test operator uses a larger sample and a 90-day window, so its newer figures should be read separately.
What does “AI resolved” mean?
In the October 2026 SaaStr account of the benchmark, automation meant a conversation was resolved with no human involvement. As SaaStr founder Jason Lemkin put it, “Automation rate here is the share of engaged conversations the AI resolved with no human involved.” The account also says that no channel deflection was required for a conversation to count as resolved.
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That is one benchmark definition, not a standard shared by every vendor dashboard or contract. Gorgias product reporting, as described in the article, separately counts an AI-resolved interaction after 72 hours without human help. Before comparing cost or reported resolution rates, get each vendor’s billable-resolution definition in writing and distinguish it from the rules used in a benchmark.
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Jason Lemkin’s SaaStr article, dated October 6, 2026, reports that Gorgias tested 13 vendors across 212 live, mid-market ecommerce stores. Each agent received the same customer messages. Responses were scored blind against 26 binary checks, with factual claims such as prices, policies, and SKUs programmatically checked against the live store. The automation figures below cover the four weeks reported in that article.
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| Vendor | Automation | Conversations |
|---|---|---|
| Rep AI | 75% | 120 |
| Decagon | 73% | 34 |
| Yuma | 71% | 118 |
| Ada | 68% | 420 |
| Gorgias | 64% | 388 |
| DigitalGenius | 50% | 561 |
| Sierra | 48% | 408 |
| Siena | 46% | 300 |
| Kodif | 44% | 188 |
| Intercom | 42% | 307 |
| Zendesk | 37% | 467 |
| Envive | 22% | 499 |
| Klaviyo | 21% | 309 |
For that same 13-vendor snapshot, SaaStr reported a 70% average for the top five, a 48% median, and a 46% conversation-volume-weighted field rate. The weighted figure gives more influence to vendors with more conversations; the median is the middle vendor result. They describe different summaries of the same limited snapshot, not competing estimates of a universal rate. See SaaStr’s October 6, 2026 report.
Why automation rate is not enough
A high closure rate can be misleading if the AI gives an inaccurate answer, fails to complete the requested action, or declares a case resolved when the customer still needs help. The October 2026 account therefore also reports blind quality scores on a 0–100 scale for several vendors:
| Vendor | Automation | Blind quality score |
|---|---|---|
| Yuma | 71% | 72 |
| Sierra | 48% | 72 |
| Decagon | 73% | 70 |
| Gorgias | 64% | 66 |
| Intercom | 42% | 66 |
| Klaviyo | 21% | 65 |
| Rep AI | 75% | 55 |
| Ada | 68% | 39 |
The contrast is material: Ada’s reported 68% automation accompanied a quality score of 39, while Gorgias reported 64% automation and a score of 66 in that same snapshot. Neither number alone tells you whether a particular vendor will work for your business; together, they show why “resolved” should be checked against answer quality and actual customer outcomes.
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Performance depended on the question
The article’s topic-level quality scores also varied. Returns policy scored 77.4; shipping policy, 74.4; modify or cancel, 63.5; order tracking, 60.1; damaged item, 59.7; and “shopper unsure,” 49.3. These are results from that benchmark, not expected scores for every store or vendor.
In the benchmark account, common failure patterns included authentication walls, repeated requests for order numbers, clarification loops, and unnecessary handoffs to a human. Policy questions can be simpler than order lookups, identity checks, or cases that require a live system action. Your ticket mix, integrations, permissions, and store configuration can therefore change the result.
How to interpret the newer benchmark figures
Gorgias’s benchmark page, refreshed in October 2026, describes a different and expanded test: 18 vendors across 228 live Shopify stores, 10,152 captured conversations, and 10,150 judged by a blind language-model evaluation. It describes a 90-day overall window, shared questions adapted to each store, cold browser sessions, and agent-initiated handoffs. Vendor ranks require at least 15 judged conversations per job.
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On that page, Gorgias reports that it ranked first overall and first for support, with 68% support automation and 74/100 support quality. These are the operator’s self-reported results for the refreshed benchmark, not an update to the older 13-vendor table. The expanded sample and 90-day window make the two snapshots unsuitable for blending into a single trend line. The Gorgias AI Agent benchmark page describes its methodology and current results.
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The page separates shopping-assistant and support-agent jobs. For support, its composite weights automation at 50%, quality at 40%, and speed at 10%; for shopping, it weights automation at 40%, quality at 35%, and speed at 25%. Its speed measure is time to a complete answer. Fast replies can improve experience, but speed is not evidence that an answer was correct or that the issue was genuinely resolved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the benchmark can—and cannot—tell you
Gorgias operated both the earlier test reported by SaaStr and the refreshed benchmark, and it ranks itself in the latter. The current page says Gorgias applies the same rubric to itself and makes its methodology public. That makes the claims more inspectable, but does not make them an independent replication. Attribute the results to the benchmark and treat vendor rankings as evidence to investigate, not a guarantee.
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The October 2026 SaaStr account says nine of ten vendors with sufficient history lost automation in the preceding four weeks. It offers model updates, changes in store configuration, or harder questions as possible explanations, but does not establish a cause. The practical point is that performance can move; a single ranking should not substitute for periodic measurement on your own cases.
How to evaluate AI support for your own tickets
- Define resolution and billing. Ask each vendor to state in writing what counts as an AI-resolved interaction, whether a later human reply changes that status, and which resolutions are billable. Keep the contractual definition separate from benchmark metrics.
- Build a representative test set. Include your real pre-sale and post-sale mix: policy questions, order tracking, cancellations or changes, damaged goods, identity checks, and requests that need live lookups or actions.
- Test the real workflow. Use cold sessions and live-store data where appropriate. Check authentication, order-number handling, integration access, action permissions, and whether the system hands off when it should.
- Score the same conversations on multiple dimensions. Track automation, answer accuracy and actionability, genuine resolution, and time to a complete answer. Review failures rather than treating every closed conversation as success.
- Repeat the evaluation. Re-run the same categories periodically and after meaningful changes to models, integrations, or store configuration. Rankings and results can shift over time.
A 65–70% automation result may be useful as a business-case target, but SaaStr presents that range as advice, not a guaranteed outcome. Your own result depends on which issues you receive and what the AI is permitted and able to do.
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