Half of U.S. consumers say they would rather do business with brands that don't use generative AI in advertising, messaging, and content. That finding, from a Gartner survey of 1,539 consumers published in March, carries direct spending implications for marketers. It lands at a moment when new research suggests marketing leaders may be overestimating how comfortable their audiences are with AI-powered brand experiences.
The same survey found that 68% of consumers frequently question whether the content they encounter is real, and 61% doubt whether the information behind everyday decisions is reliable. That level of background skepticism changes how all brand content lands, regardless of whether AI had a role in creating it. When audiences default to suspicion, every touchpoint has to earn credibility that used to be assumed.
The 40-point disconnect: A separate AMA-NY study released in March found that 82% of marketers believe consumers will benefit from AI in marketing, while only 42% of consumers agree. Consumer feedback was dominated by negative experiences with AI interactions that felt impersonal, inaccurate, or manipulative. Marketers' conversations, meanwhile, focused on strategy and automation, with only 10% addressing ethics or regulation. The gap reflects a familiar pattern: the people closest to the technology are the most optimistic about it, while the people on the receiving end remain skeptical. Closing it requires less technical refinement and more strategic restraint.
The asymmetry problem: The risk-reward calculus adds an important layer to the Gartner findings. Consumers who object to AI in brand content say it would change where they spend. Consumers who are comfortable with it are largely neutral, not choosing brands because of AI, but not penalizing them either. That imbalance suggests the strategic value of AI in customer-facing content depends heavily on execution and context. Forrester's 2026 predictions point in a similar direction, forecasting that a third of companies will damage brand trust this year by deploying AI self-service tools before the experience is ready.
The distinction that matters most for marketing teams is where AI sits in the customer experience. Back-end applications like analytics, segmentation, and A/B testing operate outside the consumer's field of vision. Front-end applications like ad copy, chatbots, and social content are a different story. That's where consumers form impressions, and increasingly, where they decide whether a brand feels trustworthy. Mapping that line across every customer touchpoint is quickly becoming a foundational exercise.
Authenticity as an asset class: EY's 2026 media trends report calls authenticity the industry's "rarest asset," noting that audiences are gravitating toward human-led storytelling and credible reporting even as AI-generated content scales. Gartner predicts that by 2027, brands will allocate half of influencer marketing budgets to content authenticity and creator credibility initiatives like identity verification and provenance checks. The market is starting to price in the trust deficit. As AI-generated output becomes the default, visible human signals like executive thought leadership, unscripted conversations, and community engagement shift from brand-building exercises to competitive differentiation.
None of these data points toward abandoning AI tools. What it does challenge is the assumption that more AI automatically means better outcomes. The strongest applications operate with intention, improving the experience in ways consumers actually value, and are deployed with the same strategic rigor as brand positioning or media planning. Trust and efficiency aren't in opposition here, but they do need to be managed together. Marketing leaders who get that balance right are well-positioned to hold attention as consumer expectations continue to evolve.
