Intercom Releases AI Sentiment Findings: Trust Is Growing, But Still Earned

Intercom dropped its 2026 AI Sentiment Report, penned by Maia Bridi, and it’s a window into how more than 1,000 everyday users actually feel about AI Agents. Announced September 9, 2026, the research highlights a clear trend: people are warming up to AI, see these tools as more capable, and—while trust is growing—it’s still not automatic. The takeaway?

Businesses can’t just coast on rising familiarity. Real trust takes thoughtful design, not just time on task.

What’s New: Sentiment Up, But Trust Depends on Context

Intercom’s data shows 49% of users now describe their experience with AI Agents as positive. That’s a nine-point uptick from 2025. Users aren’t just getting used to AI in customer service—their actual experiences are trending up, thanks to both heavier exposure and smarter AI Agents.

The survey took things a step further: midway through, folks watched a video of an AI Agent solving a real customer issue. After the demo, positive sentiment spiked to 74%—a jump of 25 points. The message is clear: nothing beats seeing the tech work in real life.

Practical proof moves the needle much more than any abstract pitch or slick ad campaign. For businesses, this means showing, not just telling, is your fastest route to buy-in.

Still, trust rides on context. While 54% are happy for AI to handle basic, routine issues, most hesitate when things get tricky or emotional. And “complex” means different things to different people: some see it as needing human judgment or exceptions to the rules; others care more about data sensitivity or whether the AI can keep track of a long, winding conversation without missing a beat.

Two main worries come up repeatedly: will the AI own up to mistakes, and can it bend when the rules need a little flex? People want to know a human is there to step in if needed. The two biggest trust boosters?

Easy escalation to a person, and clear upfront notice that they’re talking to a bot, not a human. If you want higher trust, you’ll need both better tech and better communication—one without the other falls flat.

Deeper Insights: What Users Expect from AI Agents

Ask users what would make them more comfortable with AI Agents, and you get a mix of practical demands and subtle psychological needs. “Easy escalation to a human” tops the list—it’s about control. People want a clear exit ramp if the AI gets confused or stalls out. Next up: transparency. Users want to know right away if they’re chatting with a bot. That’s about setting expectations, not tricking anyone.

Users also see AI Agents as off in their own world, with little oversight. Many are uneasy imagining these conversations happening in a vacuum, with no humans watching or stepping in. That lack of visible accountability feels risky, especially if things go sideways or if a rule needs to be bent. People want confirmation that someone—a real person—can review, correct, or override the bot’s decisions.

Another persistent concern: rigidity. Users often figure AI will stick to the script no matter what, missing the nuance or empathy a person might show. For companies, that means it’s not just a technical project to train AI Agents to spot when they’re out of their league—it’s essential for building trust.

Channel Matters: Where Users Trust AI—and Where They Don’t

The report dives into how trust shifts depending on the channel. People are most open to AI handling things over chat. That’s no surprise—chatbots and automated replies are now standard in web and app support, and users know what to expect.

But on social platforms like Twitter or Facebook, trust drops. Users there expect a human touch, and the bot can feel out of place where the conversation style is more casual or personal. For businesses, this means thinking twice before unleashing AI as your social media front line—or at least making it easy to reach a real person.

Phone support is its own beast. Those who rarely deal with AI often find voice bots maddening, especially if the system can’t handle natural speech or complex queries. Yet, some power users—those who interact with AI almost daily—actually prefer bots for routine calls, citing speed and efficiency. The upshot? Know your audience and match AI deployment to their comfort zones, not just your operational goals.

Beyond Support: Trust Questions in AI-Driven Commerce

As AI moves into shopping and recommendations, new worries surface. Some users think AI shopping assistants might put the company’s profits ahead of their own interests. They’re cautious—will the AI push higher-margin items or steer them toward options that serve the business, not the buyer?

One user summed it up: "Automatically, I am under the assumption that the AI helping me is programmed to consider the company’s bottom line and not what is in my best interest." Others are wary of bias or limited choices, suspecting the AI might not be a neutral advisor.

This hits on a broader challenge: as AI starts advising and selling, companies need to be upfront about how recommendations work and whose interests come first. If you dodge these questions, you risk losing trust—even if your AI is technically spot-on.

Why This Matters: Trust Drives Adoption—and Outcomes

If you’re rolling out AI Agents, these findings aren’t just theory. User trust directly shapes adoption, satisfaction, and ultimately, loyalty. If your AI can’t spot when to escalate or won’t admit a mistake, expect a backlash. But if users see the bot fixing real problems—and can easily get to a human when it matters—they’ll stick around.

Picture a SaaS company putting AI on the front line for basic support. If the bot can’t tell when a billing dispute needs human eyes, or pretends it never makes mistakes, frustration spikes. But with seamless escalation and honest disclosure about the bot’s role, those same customers might become fans of your digital support.

The report’s test—showing a 25-point jump in positive sentiment after users watched an AI Agent at work—proves that tangible, accountable AI wins skeptics over.

How to Apply These Lessons: Practical Trust-Building Steps

  • Design for transparency: Always disclose when users are interacting with AI, not a human. This sets clear expectations and avoids the perception of deception.
  • Enable easy escalation: Make sure users can reach a human agent quickly when needed. Consider adding visible "escalate" buttons or prompts in chat and voice interfaces, and ensure handoffs are smooth.
  • Monitor and review: Implement oversight so human staff can intervene, review AI decisions, and ensure accountability. This may involve regular audits of AI interactions and feedback loops for continuous improvement.
  • Train for flexibility: Where possible, allow AI Agents to handle exceptions or flag cases where judgment is required. Program clear rules for when to seek human input, especially in emotionally charged or ambiguous situations.
  • Audit for bias: Regularly review AI recommendations, especially in commerce, to ensure alignment with customer interests—not just company KPIs. This can help mitigate concerns about biased or self-serving suggestions.

Each of these steps ties directly to what users flagged in the report. These aren’t just nice-to-haves anymore—they’re the baseline for any business serious about AI-powered service. Build in regular user feedback, and don’t just collect it—act on it and communicate what you’re improving.

My Take: Conditional Trust Is an Opportunity, Not a Roadblock

To me, the 2026 AI Sentiment Report is a gut check for anyone designing or buying AI Agents. User comfort is moving in the right direction, but it’s fragile if you ignore the details. Trust that’s just assumed, because you’re present, won’t last. The real win is building systems where trust flows from clear disclosure, accountability, and giving users real control.

The trap? Thinking that a bump in sentiment is permanent or unconditional. The first time your AI bot drops the ball, refuses to make an exception, or stonewalls escalation, that trust vanishes.

In commerce, if buyers get the sense that the AI is just a disguised sales rep, you don’t just lose a sale—you lose the relationship. The research makes it clear: user trust in AI is fluid. Every interaction can move the needle up or down.

That’s a challenge worth tackling, not a box to check and forget.

The good news? The playbook for building trust is clear, and it’s actionable. Intercom’s findings confirm what many in the field have suspected: trust isn’t a technical inevitability—it’s a design choice.

Companies that take this seriously, investing in open, user-centered AI, will outpace those banking on familiarity alone. The reward is not just higher adoption, but stronger, more resilient relationships with your customers.

The Bottom Line: Trust Must Be Designed, Not Assumed

Intercom’s 2026 AI Sentiment Report spells it out: users are getting more comfortable with AI Agents, but their trust depends on the context and the company’s approach. If you proactively design for transparency, accountability, and easy access to a human, you’ll reap the rewards. Skip these steps, and you’re gambling with your customer relationships.

The report isn’t just industry commentary—it’s a direct call for business leaders and product teams to build the systems, processes, and communications that turn conditional trust into long-term loyalty.

To dive deeper into the findings, check out Intercom’s announcement.