AI Search Trust Is Falling Even as Usage Rises: What the 2026 Data Means for Brands
For two years the pitch on AI search has been simple: adoption is soaring, so pour your budget into being visible inside ChatGPT, Gemini, Perplexity and Google's AI answers. A study released in June 2026 by content-marketing agency Fractl complicates that story. Surveying 1,008 U.S. consumers and 150 marketers in Q2 2026, it found that the share of consumers who say AI-powered search is more helpful than traditional search collapsed from 82% in 2025 to 54% in 2026 — a 28-point drop in twelve months. Usage, meanwhile, kept climbing.
That gap is the story. People are leaning on AI search more while trusting it less, and the reasons — hallucinations, thin summaries, unverifiable claims — are exactly the things a brand's content can either worsen or fix. This piece unpacks the numbers, explains why trust is eroding even as usage grows, and lays out what marketers should actually do about the "trust tax" now attached to AI visibility. The findings were reported independently by Search Engine Land and detailed in Fractl's own 2026 data release.
The trust paradox in numbers
The headline is not that AI search is failing — it clearly isn't in adoption terms. It's that the same audience using it more is judging it more harshly. Here is the year-over-year shift the study measured.
| Signal | 2025 | 2026 |
|---|---|---|
| Say AI search is more helpful than traditional search | 82% | 54% |
| "Skeptics" — rate AI search less helpful than traditional | 3% | 17% |
| Say heavy AI use would reduce trust in a favorite brand | 20% | 39% |
| Using AI tools for search more than a year ago | — | 70% |
Read those rows together and the paradox is stark. Seventy percent of consumers say they use AI search more than last year while only 3% say they use it less — yet the group that actively rates AI worse than a classic search results page grew nearly six-fold, from 3% to 17%. This is not early-adopter enthusiasm cooling into indifference; it's a maturing audience that has been burned enough times to become discerning.
The brand-trust line is the one that should make marketers sit up. In 2025, one in five consumers said a brand leaning heavily on AI would lose their trust. In 2026 it's two in five — distrust of AI-heavy brands roughly doubled in a year. And it skews by audience: 54% of Gen Z say heavy AI use decreases their trust in a brand versus 32% of baby boomers, and women penalize it more than men (44% vs. 34%). If your customers are young or female, the trust tax is higher.
Why trust is dropping while usage grows
Three forces show up repeatedly in the data, and all three are things content and marketing teams influence directly.
1. Hallucinations have a cumulative cost
AI search first felt like a frictionless instant-answer machine. But repeated exposure to confident-but-wrong answers, unsupported claims and generic summaries trains users to double-check. The study found 27% of marketers say their own brand has already been inaccurately described or misrepresented in an AI-generated response, and 14% say those inaccuracies hit customer relationships, sales or PR. Every bad answer is a small withdrawal from a shared trust account — and the balance is now visibly falling.
2. Consumers no longer take a single answer at face value
Rather than accept one AI summary, consumers now triangulate: the study found they check an average of 2.4 platforms before making a purchase. Google still leads for purchase decisions (39%), followed by Reddit (15%) and AI tools (14%); YouTube has overtaken Google for how-to content (50% vs. Google's share). The behavior that AI search was supposed to kill — cross-checking sources — is actually intensifying because people don't fully trust the machine's first pass.
3. Disclosure expectations are outrunning brand behavior
Consumers increasingly want to know when they're looking at AI output. More than 80% want AI-generated content labeled across every format — 91% for video, 90% for images, 87% for audio and 84% for written content — yet only about 20% of brands consistently disclose AI use. That gap between what audiences expect and what brands actually do is itself a trust liability waiting to be triggered.
The marketer's dilemma: adopt fast, verify slowly
The other half of the survey — the 150 marketers — reveals why so many bad AI answers are reaching consumers in the first place. AI now touches 53% of marketing work, up from 38% a year earlier, and 59% of marketers report a 7-out-of-10-or-higher pressure to adopt it. But the governance hasn't kept pace.
| What marketers report | Share |
|---|---|
| AI made work faster and better | 26% |
| AI made work faster but more generic | 47% |
| Publish AI content without fact-checking or legal review | ~50% |
| Track their brand's visibility inside LLMs | 24% |
| Prioritize original research as a strategy | 15% |
The picture is a race to publish faster without the checks that keep quality — and therefore trust — intact. Nearly half of marketers ship AI-assisted content with no fact-check or legal review, and only a quarter track how their brand shows up in AI answers at all. Original research, the single thing an AI model cannot manufacture, is the least prioritized strategy at 15%. In other words, marketers are collectively feeding the generic-answer problem that is driving consumer trust down.
What to do about the trust tax
The strategic conclusion isn't "abandon AI search." Adoption is real and growing, and roughly half of sites in the study already report organic traffic declines they attribute to AI Overviews — you can't opt out of the surface. The move is to compete on the axis where the field is weakest: trustworthiness. Here's how.
1. Audit how AI describes your brand — regularly
With 27% of brands already misrepresented in AI answers, catching a hallucination before it reaches a customer is table stakes. Run your priority prompts across ChatGPT, Gemini, Perplexity and Google AI Mode on a schedule, log what they say about you, and correct the source pages the models are pulling from. This is exactly what dedicated monitoring exists for — see our roundup of the best AI visibility tracking tools in 2026 to automate it rather than checking by hand.
2. Invest in original research and named expertise
Original data is the least-used strategy (15%) and the most defensible: a model can paraphrase everyone's generic guide, but it can't invent your proprietary survey, benchmark or case study. Pair that with visible, credentialed authorship. Trust signals like real authors, transparent sourcing and demonstrable experience are precisely what earn citations when a model can't lean on brand size — the mechanics of which we cover in E-E-A-T for AI search.
3. Show your sources on the page
If consumers are cross-checking 2.4 platforms because they distrust a single answer, be the page that makes verification easy. Cite primary sources, link to the studies and data you reference, date your claims, and state clearly where a number came from. Content that is transparently sourced is both more citable by AI engines and more reassuring to the human who clicks through to check.
4. Put governance in writing before you scale AI content
Half of marketers publish AI content unchecked; the fix is boring but decisive. Make fact-checking, human review, a hallucination-escalation path, disclosure rules and brand-safety review standing operating procedures — not one-off favors. The teams that formalize this now avoid being the 14% whose AI inaccuracies damage sales later.
5. Build topical depth, not thin coverage
Generic, shallow content is exactly what's eroding trust in AI answers — and publishing more of it makes you part of the problem and easy to ignore. Depth and consistent coverage of a subject are what make a brand a reliable, quotable authority over time; our guide to building topical authority in 2026 walks through doing it deliberately.
The mistakes to avoid
- Reading rising usage as rising trust. They've decoupled. Usage is up 70% year-over-year while "AI is more helpful" fell from 82% to 54%. Plan for a skeptical audience, not a captive one.
- Publishing AI content unchecked to move faster. Half the industry does it, and it's the direct cause of the hallucinations driving distrust. Speed without verification is a trust liability.
- Ignoring AI misrepresentation of your brand. With 27% of brands already misdescribed and only 24% tracking LLM visibility, most companies won't notice the damage until a customer does.
- Skipping disclosure. Over 80% of consumers want AI content labeled; only ~20% of brands comply. That gap becomes a reputation problem the moment it's discovered.
- Competing on volume. When 47% of marketers admit AI made their output more generic, adding more generic content wins nothing. Original research and verifiable expertise are the scarce, defensible assets.
The takeaway
Fractl's 2026 data marks a turning point: AI search has moved past its honeymoon. Consumers use it more but believe it less, and they now actively penalize brands they see as leaning on AI without care — a penalty that is heaviest among younger and female audiences. For marketers, the winning response isn't to chase AI visibility harder or to retreat from it, but to win on the dimension the whole field is neglecting. Verify what AI says about you, publish original and transparently sourced work, disclose AI use, and build genuine topical depth. In a market where trust is the falling metric, being the brand people can trust is the clearest competitive edge left.
