CTA’s new consumer research gives technology sellers a reason to focus on useful tasks. It does not establish that people will pay more for an AI-branded device. Being willing to try assistance, trusting it with personal information and buying a product are different decisions.

The September 30 announcement describes openness to practical AI convenience alongside mixed overall sentiment. For someone evaluating the next CES pitch, the relevant question is which part of that story the product can actually deliver.

Read the sample before the sales slide

CTA’s public study preview describes an online survey of 2,000 US adults, conducted July 2–15, who had seen or heard at least something about AI. Results generally concern that familiarity-qualified group unless stated otherwise. CTA applied demographic weighting.

That screening matters. A finding among people familiar with AI should not be retold as a finding among every adult. The preview also redacts some results; the complete study was not available for this article. Missing figures cannot responsibly be reconstructed from a promotional summary.

Survey openness is a useful starting point for a product hypothesis. It is not an observed purchase, a retention measure or evidence that a particular feature performs well.

Use and concern can coexist

Independent Pew Research Center research, published June 17, also describes growing chatbot use alongside substantial concern about AI’s impact and personal information. Its survey included 5,119 US adults in February through the American Trends Panel.

The two studies use different populations, questions and field dates. Their percentages should not be put side by side as if they measure the same thing. Pew provides context for the broader point: someone can find a tool helpful and still dislike its risks or the direction of the industry.

For sellers, that complicates the assumption that familiarity automatically produces enthusiasm. For customers, it means there is no need to adopt an all-or-nothing position on AI. A useful feature can earn its place through a specific benefit while another feature remains disabled.

Ask what changes after the trial

A hypothetical household might welcome an assistant that summarizes an appliance manual. That does not tell us whether it will pay a recurring fee, connect private accounts or replace working hardware to get the same function. Each extra requirement changes the decision.

A good product demonstration should therefore reveal the actual task, required data, added cost and route back to ordinary use. Ask to see an error handled, not just a successful prompt. If correcting the assistant requires more effort than doing the task directly, convenience has not been demonstrated for that user.

An organizational buyer can take a similar approach with a limited evaluation: define a task, measure acceptable completed work and include review time. Separate initial curiosity from sustained use. These are evaluation recommendations, not results established by CTA’s survey.

Vendors offering understandable value stand to gain from that discipline. Those depending mainly on an AI label have more to prove. Customers could lose through subscriptions or replacement purchases that survive long after the novelty fades.

The research and the policy pitch have different jobs

CTA is both an industry research producer and an advocate for its members. The announcement also promotes its preferred AI policy framework. That advocacy is not enacted law, and the survey is not a test of whether the proposed framework would deliver its promised effects.

Before CES 2027, watch for product-specific evidence: real usage after onboarding, transparent costs and clearly documented handling of mistakes. Those measures would make the general story of consumer openness more meaningful for a buying decision.

Frequently Asked Questions

Who participated in CTA’s survey?

Its main sample was 2,000 online US adults familiar with AI, surveyed July 2–15, 2026. Check the stated base before applying an individual result more broadly.

Does willingness to try AI predict willingness to pay?

It does not establish that outcome. Pricing, data access, alternative tools and the actual product experience still need evaluation.

Do CTA and Pew show contradictory results?

A direct comparison is not justified by these reports. They ask different questions of different samples at different times; useful adoption and concern can coexist.

What should I ask during a CES product demonstration?

Ask what task improves, what it costs, which data it needs and how a mistake is corrected. Request evidence for that particular product rather than a general market survey.