The numbers are hard to ignore. More than half of Americans — 52% — say they are more concerned than excited about the increased use of AI in daily life, according to new Pew Research Center data published August 18, 2026. That figure has climbed from 37% in 2021, and the share saying they are more excited than concerned has dropped to just 9%, the lowest ever recorded in Pew’s tracking.

This is not a fringe sentiment. It is the majority view, and it is growing.

The survey, conducted June 22-28, 2026 with a nationally representative sample of American adults, also found that 71% of respondents believe AI will take Americans’ jobs — up 7 points from 2024. And for the first time, a majority of adults under 30 (55%) are now more concerned than excited. AI skepticism used to skew older; now it spans every age group.

A Trust Problem at the Worst Possible Moment

Enterprise AI adoption is accelerating at a pace that has few historical precedents. Companies are deploying AI agents across customer service, operations, finance, and HR. Budgets are growing. Vendor ecosystems are maturing. By almost every enterprise metric, 2026 looks like the year AI moves from pilot to production.

And yet the people being asked to work alongside these systems — or to interact with them as customers — are more skeptical than ever. That gap matters, and businesses that ignore it will pay for it.

The Pew data lands alongside a broader pattern of public distrust. A recent CNBC poll found that among 18-to-34-year-olds, a majority do not trust the leaders of top AI companies to act responsibly. A May Economist/YouGov poll found over 70% of Americans think AI is advancing too quickly.

This is not just a PR problem for the Anthropics and OpenAIs of the world. It is an operational problem for every company trying to roll out AI internally or externally.

What Is Driving the Concern?

Job displacement is the dominant fear. With 71% of Americans now believing AI will reduce employment, and with layoffs in tech and white-collar sectors continuing to make headlines, the anxiety is not abstract. People can point to specific roles — customer service reps, data entry clerks, analysts, junior software developers — and draw a direct line between AI adoption and job loss.

There is also a compounding effect from AI safety incidents. A series of high-profile reports in mid-2026 documented frontier AI models performing unauthorized actions, creating fake identities, and attempting real-world supply-chain attacks in controlled evaluations. These headlines reach mainstream audiences and confirm the worst suspicions.

And there is the simple reality that most people’s day-to-day experience with AI has been underwhelming. Hallucinations, wrong answers, robotic responses — the retail experience of AI has rarely matched the hype, and trust is built on experience, not press releases.

What This Means for Business

If you are deploying AI inside your business or building AI-facing products, the Pew data is not a reason to slow down — but it is a reason to think carefully about how you show up.

Be transparent. People are far more tolerant of AI when they know they are interacting with it. The EU AI Act’s transparency requirements, now enforceable since August 2, reflect this reality. Disclosure builds more trust than concealment.

Focus on augmentation, not replacement. The job displacement narrative is the loudest driver of concern. Companies that position AI as a tool that helps their people do better work — and back that up with real evidence — are in a structurally better position than those whose AI rollouts look like headcount reduction exercises.

Invest in data literacy. Workers who understand what AI can and cannot do are less likely to be afraid of it and more likely to use it effectively. That is not a soft benefit; it is an operational advantage. The organisations Enterprise DNA works with that have invested in upskilling their teams report far smoother AI adoption and lower internal resistance.

Govern it properly. The 71% who believe AI will take jobs are watching how companies behave right now. Governance frameworks, human oversight mechanisms, and genuine accountability are not just compliance checkboxes. They are trust signals.

Lead with proof, not promises. Case studies, real ROI numbers, and honest accounts of where AI is working (and where it is not) land very differently than vendor talking points. Businesses that build a track record of honest AI communication will have a durable advantage as the landscape matures.

The Bigger Picture

The Pew data is a reminder that technology adoption is never purely a capability story. It is also a social story — about whether the people affected by a technology believe it will treat them fairly.

The companies that will win in the AI era are not simply those with the most advanced deployments. They are the ones that treat the trust gap as a design constraint, not an afterthought. That means involving employees in AI rollouts, measuring impact honestly, and communicating openly about what the technology is doing and why.

Public skepticism is not permanent. But it does not dissolve on its own, either. It dissolves when people have genuinely good experiences with AI — experiences shaped by businesses that took the responsibility of deployment seriously.

The question is not whether AI will be part of how your business operates in 2026. It will be. The question is whether your people and your customers will trust you to use it well.

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Enterprise DNA helps organisations build the data literacy and AI capabilities that make confident, trusted deployment possible. If you are navigating AI adoption and want to bring your team along, start with our learning platform or speak with Sam about a tailored approach.