What US Policyholders Actually Trust AI to Do (And What They Don't)

Trust Is Growing, But It Isn't Universal

Consumer comfort with AI in insurance moved fast over the past year — support for insurers' use of AI automation roughly doubled year over year among US consumers surveyed in 2026. That's a real shift, and it's opening the door for insurers to automate more of the customer experience than would have been acceptable even two years ago. But the same research makes something else clear: trust has sharp boundaries, and they don't move in a straight line with how comfortable people are with AI in general.

A meaningful share of consumers are comfortable with AI automation for routine tasks like generating a quote. A much smaller share are comfortable with AI making decisions that materially affect them — and insurers who don't respect that distinction risk turning an efficiency win into a trust problem.

Where Automation Is Welcomed

Quote generation and routine account tasks. Consumers are broadly comfortable with AI handling straightforward, low-stakes tasks — getting a quote, updating contact information, checking a policy detail. These are transactional interactions where speed is valued more than the reassurance of a human on the other end.

Proactive status updates. Automated, milestone-based communication throughout a claims process — rather than silence until someone calls to ask — consistently improves satisfaction. This is automation that reduces friction without touching a decision that matters to the customer.

Document and photo processing. Customers generally don't mind AI reading a submitted repair estimate or processing claim photos, because the underlying decision — whether the claim is approved — still visibly involves human accountability from the customer's perspective.

Where Trust Drops Sharply

Claims denials and coverage decisions. This is where consumer comfort falls off fastest. People are far less willing to accept a denial or a materially reduced payout from a system they can't see inside or argue with the way they could a human adjuster. Insurers automating this stage without a clear, visible human review layer are taking on real reputational risk, regardless of how accurate the underlying model is.

Anything that feels like it removes recourse. Consumers tend to be less concerned about AI being involved than about not having a clear path to a human when something feels wrong. The trust gap isn't really about the technology — it's about whether the customer believes they still have somewhere to go if the system gets it wrong.

Opaque reasoning. When an AI-driven decision can't be explained in plain language — why a premium increased, why a claim was only partially covered — that lack of explanation erodes trust faster than the decision itself often does.

What This Means for Insurance AI Strategy

The winning approach isn't choosing between automation and trust — it's designing automation that's honest about where the human accountability sits. That means:

• Being transparent with customers about where AI is involved in their experience, rather than obscuring it
• Keeping a visible, easy-to-reach human review path for denials and high-stakes decisions
• Making AI-driven decisions explainable in plain language, not just accurate
• Automating the friction (status updates, routine transactions) more aggressively than the judgment calls (denials, disputes)

Where to Start

If your organization is expanding AI into the customer experience, map your use cases against this trust gradient before deciding what to automate next. The highest-value, lowest-risk opportunities are almost always in the routine and communication layer — automating there builds both efficiency and trust before you approach the harder, higher-stakes decisions.