Often, yes. And the number who mind is rising.
The share of people who say AI-led service costs a business their trust went from 47% to 57% in eight months. That is a fast move on a question this fundamental, and it should change how you think about deployment rather than whether to deploy.
What people are actually objecting to
Not the software. Being stuck.
Nobody minds a machine that resolves their problem in twenty seconds at 11pm. What people object to is the experience they have been trained to expect: a bot that cannot help, will not let them past, and asks them to rephrase.
That is a decade of decision trees teaching customers a reflex, and any new deployment inherits it whether it deserves to or not.
The tells
In a badly built agent, people spot it within two exchanges. Repetition, a suspiciously uniform tone, an inability to handle anything specific about their account.
In a well built one, the tell is usually not the language at all. Modern models write fine. It is the moment the conversation needs something real, a live balance or an actual install date, and nothing comes back.
Which is another way of saying: the thing that makes an agent convincing is the same thing that makes it useful. Integration, not phrasing.
Why hiding it is the wrong instinct
The temptation is a human name and no disclosure. It works right up until it does not, and the moment a customer works it out, the deception becomes the story rather than the service.
Being told upfront costs you almost nothing. Being caught costs you the interaction and some of the relationship.
Our own position is that the agent says what it is and the route to a person stays visible. That is partly principle and partly self-interest, because the alternative fails badly at exactly the moment a customer is already annoyed.
What the satisfaction data says
More encouraging than the trust numbers suggest. Industry-average CSAT for AI support agents sits around 78%, with leaders above 85%, roughly level with live chat. One large analysis put pure-AI handling at 4.1 out of 5 against 4.3 for human agents, and hybrid flows with a clean handover narrowed that to almost nothing.
So the ceiling is high. The variance is what should worry you, and it tracks integration depth rather than model quality.
The practical read
Assume they can tell. Design as if they can.
That means disclosing it, keeping the human route obvious, grounding every answer in real data so the agent can actually help, and escalating early enough that nobody feels trapped.
Do that and the fact it is AI stops being the point. Fail on the last one and no amount of conversational polish saves it.
