How is your AI support actually doing?
Not the number in the vendor's slide deck. Not the demo. Yours — on your real traffic, this quarter.
If you run AI customer service, you've probably tried to answer some version of these four questions:
→ Is it truly resolving tickets, or just deflecting them until the customer gives up or a human quietly cleans it up?
→ What's the ROI really looking like once you net out the cost of the platform, the setup, and the escalations?
→ Is there still room to improve — or are you already near the ceiling for the kind of traffic you handle?
→ And how do you actually compare with everyone else running the same play in your industry?
Here's the uncomfortable part: almost nobody can answer these with confidence. And it's not because support leaders aren't paying attention. It's because the ground truth doesn't exist in any shared, honest form.
Start with the word "resolution." It sounds precise. It isn't. One vendor counts a ticket as resolved the moment the bot sends a reply. Another counts it only if the customer never writes back. A third quietly folds deflection — the customer bouncing off a help article — into the same bucket and calls it automation. So when you see "85% resolution" on a landing page, you have no idea whether that's a genuinely closed issue or a cleverly drawn chart. The definition bends to flatter whoever is publishing it.
That makes comparison almost impossible. If you're sitting at 60%, is that world-class for your vertical, or embarrassing? You genuinely can't tell — because there's no shared denominator, no neutral reference point, and every benchmark you can find was published by someone with a reason to make their own number look good.
That lack of transparency is the problem we set out to close.
We built a benchmark tool, and we deliberately backed it with two different kinds of data:
- Public data — independent 2026 cross-program aggregates, pulled from sources that aren't trying to sell you anything.
- Private data — real field deployments, where we can see what actually happens once a system is live on messy, real-world traffic.
The reason we use both matters. Public data keeps us honest and broad. Private data keeps us grounded in what really happens after launch — not the pilot, not the demo, but month six, when the easy intents are automated and the hard ones are all that's left.
Here's how it works. You pick your industry — ecommerce, fintech, SaaS, travel, healthcare, telecom, and so on — and the tool shows you the realistic resolution and CSAT range for that vertical. Not a single vanity figure, but a band: the low end, the median, and the top-10% world-class mark. Then you can enter your own current resolution rate and CSAT, and it places you directly on that scale. In about ten seconds you can see whether you're lagging, sitting at the median, or genuinely at the front of your industry.
And because a number without context is just anxiety, the tool also breaks down the four factors that decide where any given deployment actually lands:
→ Industry complexity. How ambiguous, regulated, emotional, or multi-step your intents are. This sets the ceiling. Structured, data-rich intents like order status resolve high; regulated or emotional ones resolve far lower, no matter which vendor you use.
→ System capability. How strong the underlying platform is — multi-agent reasoning, backend actions, retrieval, guardrails. A more capable system reaches higher within the same industry band and holds quality as complexity rises.
→ Maturity of your assets and playbooks. The state of your knowledge base, SOPs, and escalation rules. Most deployments launch around 40–50% and climb past 60% over six to twelve months as those assets mature. If you're new, low isn't failure — it's the starting line.
→ Transparency of your data. How accessible the orders, accounts, and history are that the AI needs to actually resolve a ticket. When the system can see everything, it resolves. When data is siloed, even simple intents stall.
The honest caveat: these ranges are directional, not audited. Definitions of "resolution" vary by source, so we treat the numbers as a realistic target band — something to steer by — rather than a certified figure. We'd rather tell you that plainly than pretend to a precision the whole industry can't actually deliver yet.
This is a living benchmark. We'll keep updating it as more public data lands and more deployments mature, and we expect the ranges to sharpen over time. If your team has been looking for a bit of honest transparency in a space that badly lacks it, we hope this gives you a real reference point to plan against — whether or not you ever talk to us.
Try it here: https://aissist.io/tools/benchmark
And I'd genuinely value your feedback. If the range for your industry feels off compared to what you're seeing in your own numbers, tell me — that's exactly the kind of signal that makes the next version better for everyone.
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