A fresh study from research firm Gartner reveals a stark divide in how American shoppers view the growing wave of generative AI (GenAI) in marketing. Half of the 1,539 respondents surveyed in October 2025 said they would rather do business with brands that do not employ GenAI in any consumer‑facing messages, advertisements, or content. The poll, conducted among U.S. adults, frames “brands that use GenAI” specifically as those that embed the technology in outward‑looking communications.
The headline figure—50 % of consumers preferring a “no‑AI” approach—contrasts sharply with the rapid rollout of AI‑driven creative tools across the ad tech ecosystem. While many agencies tout the speed, personalization, and cost efficiencies of AI‑generated copy and visuals, the data suggests a sizable portion of the market remains wary.
Trust is the new currency
Beyond the headline preference, the survey uncovers a broader erosion of confidence in digital information. Sixty‑one percent of participants reported that they frequently question whether the data they rely on for everyday decisions is trustworthy, and 68 % admit to often doubting the authenticity of the content they encounter online. These figures point to a growing skepticism that extends well beyond AI‑specific concerns.
By the close of 2025, only 27 % of respondents said they still rely on gut feeling—or intuition—to judge whether a piece of information is true. The shift toward “independent checking” and verification behaviors underscores a consumer base that is actively seeking ways to confirm the veracity of what they read, watch, or hear.
Analyst perspective: AI as a trust decision
Emily Weiss, Senior Principal Analyst for Gartner’s Marketing practice, frames the findings as a “trust decision as much as a technology decision.” In her words:
“Consumers are questioning what’s real and making efforts to verify more of what they see. The brands that win will be the ones that use AI in ways customers can immediately recognize as helpful, while being transparent about when AI is used, what it’s doing, and giving customers a clear choice to opt out.”
Weiss’s assessment aligns with a broader industry dialogue that places transparency and user control at the forefront of responsible AI deployment. As marketers chase the efficiency gains promised by generative models, the pressure to disclose AI involvement—and to do so in a manner that feels beneficial rather than intrusive—has never been higher.
What “optional” AI looks like in practice
Weiss advises brands to treat GenAI as an optional layer rather than a mandatory engine. The recommendation is to start with “clearly assistive” use cases that deliver immediate, measurable value to the consumer. Examples could include AI‑enhanced product recommendations that are clearly labeled, or copy suggestions that are presented as drafts for human editors to refine.
The analyst also stresses the importance of “labeling AI‑driven experiences,” ensuring that end users understand when they are interacting with machine‑generated content. By coupling transparent labeling with easy avenues for verification—such as linking to source data, providing proof points, or offering a straightforward opt‑out—brands can mitigate the trust deficit highlighted by the survey.
The stakes for CMOs in 2026 and beyond
Gartner’s forthcoming report, What CMOs Must Know About Consumers in 2026, is expected to dive deeper into the implications of these trust dynamics for senior marketing leaders. The core message is clear: the era of unbridled AI experimentation is giving way to a more cautious, consumer‑centric approach.
For chief marketing officers, the data translates into a strategic imperative to balance innovation with responsibility. Campaigns that lean heavily on AI‑generated visuals or copy without clear attribution risk alienating a sizable segment of the market. Conversely, brands that embed verification mechanisms and maintain human oversight may find a competitive edge in an environment where authenticity is prized.
Competitive context: AI adoption across the ad tech stack
While the Gartner survey zeroes in on consumer perception, the broader ad tech landscape continues to integrate generative models at scale. Programmatic platforms, creative studios, and demand‑side partners are all experimenting with AI‑based asset generation, dynamic creative optimization, and automated copywriting. The technology promises faster turnaround times and hyper‑personalized messaging—attributes that have traditionally been hallmarks of high‑performing campaigns.
However, the new data suggests that the industry’s “move fast and break things” mindset may need recalibration. Brands that adopt a “human‑in‑the‑loop” model—where AI assists but does not replace editorial judgment—are more likely to align with the trust expectations emerging from today’s shoppers.
Regulatory undercurrents and future compliance
The rising consumer skepticism dovetails with increasing regulatory scrutiny around AI disclosures. While the United States has yet to enact a comprehensive federal AI labeling law, several states are exploring legislation that would require clear identification of AI‑generated content in advertising. Europe’s AI Act, already in effect for high‑risk systems, also nudges marketers toward transparency.
In this evolving legal environment, the Gartner findings provide an early warning: brands that proactively label AI usage may not only avoid potential fines but also position themselves as trustworthy custodians of consumer data and experience.
Practical steps for marketers today
Based on the survey and analyst commentary, marketers can consider the following actions:
- Audit existing AI deployments – Identify every touchpoint where GenAI contributes to consumer‑facing content and assess the level of disclosure currently in place.
- Implement clear labeling – Adopt a consistent visual or textual cue that signals AI involvement, similar to “Sponsored” tags for paid content.
- Offer opt‑out mechanisms – Provide users with a straightforward way to receive non‑AI content, whether through preference settings or direct communication channels.
- Back claims with evidence – Whenever AI‑generated statements are used (e.g., performance statistics, product benefits), attach verifiable data sources or third‑party certifications.
- Educate internal teams – Ensure copywriters, designers, and campaign managers understand the importance of transparency and the potential reputational risks of undisclosed AI usage.
By embedding these practices into campaign workflows, brands can begin to rebuild the trust gap highlighted by Gartner’s data.
The broader business impact
The survey’s 50 % split signals a potential market segmentation: one half of consumers may gravitate toward brands that emphasize human craftsmanship, while the other half may be more accepting of AI‑enhanced experiences. For B2B marketers, this bifurcation translates into a need for nuanced audience targeting and messaging strategies.
Companies that sell AI tools to marketers may also feel the ripple effect. If end‑users demand transparency, vendors will likely develop built‑in labeling features, audit trails, and compliance dashboards to help their clients meet these expectations. The demand for such “trust‑by‑design” capabilities could become a new revenue stream within the ad tech ecosystem.
Looking ahead
The Gartner survey paints a portrait of a consumer base that is increasingly savvy, skeptical, and eager for verification. As generative AI continues to mature, the pressure on marketers to demonstrate ethical usage will intensify. Brands that can blend the efficiency of AI with clear, user‑centric transparency are poised to capture the trust—and the spend—of the modern shopper.
For CMOs, the message is unequivocal: the next wave of successful marketing will be measured not just by click‑through rates or conversion metrics, but by the degree to which audiences feel informed, respected, and confident in the content they receive.
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