Somewhere on your floor, there’s probably an agent who’s muted the AI panel. Not because it’s wrong often. Usually the opposite: it’s accurate more often than not, and they still don’t look at it. If you’ve rolled out any kind of agent assist tool, you’ve likely seen a version of this. It’s one of the most common and least discussed problems in contact center AI, and it has almost nothing to do with how good the AI actually is.

The tool works. Agents still ignore it.

We wrote previously about the difference between institutional trust in AI, the kind compliance officers care about, and the practical question of whether a system is safe to plug into a regulated workflow. That’s a real and important question. But it’s a different one from whether the agent sitting at the desk, mid-call, with a customer waiting, actually looks at what the AI is suggesting.

That second kind of trust is earned or lost in the small moments: a suggestion that felt off once, a prompt that arrived a beat too late, a feeling of being watched rather than helped. Get enough of those moments, and agents route around the tool entirely, quietly, without ever raising it as a problem.

What the research says about trust at work

This isn’t unique to contact centers. A 2025 global study by KPMG and the University of Melbourne, surveying more than 48,000 people across 47 countries, found that only 46% of employees were willing to trust AI systems at work. More telling: 56% admitted they’d made a mistake at work because they relied on AI output without properly checking it, and only two in five had received any training on how to use AI tools at all.

That last number matters more than it might seem. A lot of what gets labeled “agents don’t trust the AI” is actually “agents were handed a new tool with no explanation of what it’s good at, what it isn’t, and what to do when it’s wrong.” Trust and training aren’t separate problems here. They’re the same problem wearing two names.

Why contact center agents are especially skeptical

Contact center agents have a specific reason to be more cautious than the average knowledge worker: they’re the ones who answer for a bad suggestion, not the AI. If a prompt leads them to say something wrong to a customer, they’re the one on the recording, not the system that suggested it. That asymmetry breeds caution, and reasonably so.

There’s also a simpler issue. Many agents have already lived through a tool rollout that promised to make their job easier and instead added a step. A panel that has to be checked and dismissed on every call is friction, not help, unless what it offers is clearly faster than what the agent already knows how to do. Trust, in that sense, is really a judgment about speed and relevance, made in the first two or three uses.

Suggest, don’t dictate: how Agent Assist is built around this

This is the design principle behind RepsMate Agent Assist: it surfaces relevant information during a live call, but it doesn’t script the conversation or interrupt the agent’s judgment. An experienced agent who knows the answer isn’t slowed down by a prompt they don’t need. A newer agent who’s uncertain has something to glance at without breaking the flow of the call.

There’s also a reason we recommend starting with Data Answer before layering in Agent Assist, which we covered in Data Answer vs. Agent Assist: which AI tier to adopt first. Agents who’ve already used a reactive tool, one they type a question into and get a document-grounded answer from, tend to arrive at a proactive, real-time tool with a working sense of what it’s good at and where its limits are. That familiarity is most of what trust is built from.

What managers can do in week one

A few things consistently make the difference between agents who adopt an assist tool and agents who quietly turn it off:

• Explain what it can’t see. Agents trust a tool more when they understand its boundaries. A system that says “I don’t know” instead of guessing and that pulls only from your actual documents rather than the open internet is easier to trust precisely because its limits are visible.
• Show, don’t just tell, in week one. A five-minute walkthrough of a real call, using a real suggestion, does more than a slide deck about the tool’s accuracy rate.
• Give agents a way to rate answers, and actually close the loop. A thumbs up or down on every suggestion turns “ask agents what felt off” into a daily habit instead of a one-time survey. What builds trust isn’t the rating itself, it’s agents seeing that a thumbs down leads to the source document getting fixed within days, not sitting in a queue nobody looks at. We cover this same loop from the documentation side in one week to live: what actually happens when you roll out Data Answer, and it’s worth repeating here: the fix isn’t just closing a ticket, it’s making the next answer correct and complete, which is exactly what turns a skeptical agent into an advocate. Reassurance from management doesn’t do that on its own. A visibly fixed answer does.
• Don’t tie early usage metrics to performance reviews. Nothing kills honest adoption faster than agents suspecting the tool is also there to monitor them.

A fuller picture when you pair it with call evaluation

There’s a second benefit to this feedback loop that’s worth mentioning specifically to your QA team, not just your documentation owners. When you evaluate calls through your existing QA platform alongside Data Answer’s usage logs, you get a far richer picture than either view gives you on its own, because you can tie a specific question an agent asked, or a moment of hesitation, directly to the actual call it happened during.

Instead of a low-confidence answer or a documentation gap showing up as an isolated data point, you can see the real conversation around it: what the customer was actually asking, how the agent handled that moment, and whether the answer that eventually came through landed. That context is often what turns “agents don’t seem to trust this” from a vague impression into something specific enough to actually fix, whether that means rewriting a document, retraining on a particular scenario, or discovering the suggestion was fine and the hesitation was about something else entirely.

From reactive trust to proactive trust

The pattern we see most often: teams that start with Data Answer build a baseline of trust in a low-stakes way, an agent types a question, gets a grounded answer, and over a few weeks learns the system won’t feed them something made up. That baseline is what makes the jump to Agent Assist, where the AI is speaking up during the call rather than waiting to be asked, land as help rather than surveillance.

Trust, in other words, isn’t a feature you ship. It’s a sequence you design for.

FAQ

Is Agent Assist listening to calls agents don’t know about? No. Agents know when the tool is active, and it’s built to support the call in progress, not to monitor performance in the background.

Will agents feel like they’re being watched? That risk is real if usage data gets tied to performance reviews early on. We recommend keeping adoption metrics separate from evaluation, at least until agents trust the tool on its own terms.

How is Agent Assist different from a script agents have to follow? It’s not a script. It surfaces relevant information and next steps, but the agent decides what to say and how to say it. Nothing is enforced or read verbatim.

Want to see what this actually looks like mid-call? Book a demo, and we’ll show you exactly what agents see, not just what the dashboard shows leadership.