Six in ten US consumers actively dislike seeing the word "AI" in advertising and marketing, according to a WordPress VIP survey published in June 2024. The same survey found that 86% of consumers don't fully trust AI systems. These numbers expose a widening gap between where companies are pouring money and what the people buying their products actually think about it.

Enterprise AI budgets have grown sharply over the past two years, and marketing teams have seized on AI as a competitive advantage — splashing "AI-powered" across product descriptions, campaign creative, and customer service interactions. The WordPress VIP data suggests this strategy may be backfiring with a substantial portion of the intended audience.

The trust deficit makes sense in context. Consumers have witnessed high-profile AI failures in real time: legal citations that were fabricated, product recommendations that don't exist, chatbots that loop endlessly without resolving anything. Each incident erodes the baseline credibility a new technology needs to build before people will widely adopt it. The 86% distrust figure reflects this accumulated damage — not a rejection of automation itself, but a rational wariness toward systems that have visibly failed in public.

The 60% "turnoff" number deserves special attention for anyone in marketing or product. It separates two distinct problems: people's lack of trust in AI versus their aversion to AI labeling. A consumer can accept an AI-assisted tool while still finding the "AI-powered" label off-putting. That matters because it means the messaging problem and the trust problem are related but separate. Putting "AI" everywhere in your product copy may backfire even when the underlying capability is genuinely useful.

We saw a similar pattern with cloud computing in the early 2010s. The phrase "cloud-powered" was technically accurate but gradually became marketing noise. Companies eventually dropped it from consumer-facing copy in favor of describing what the technology actually did — faster, cheaper, more reliable. AI branding may be following the same trajectory, except faster, because people saw AI's failure modes early and they were highly visible.

Underneath the marketing question sits a deeper structural problem. If 86% of consumers don't fully trust AI, then any team building AI-native tools faces an adoption hurdle that better interface design alone cannot fix. Rebuilding that trust at scale takes real track records, honesty about what the AI can and cannot do, and reliable results over time — not polish and speed improvements. Organizations that understand this tend to invest in explainability (showing users why the AI made a decision) and human escalation paths (letting a person take over when something goes wrong), not just in reducing response time or expanding the amount of context the AI can consider.

The WordPress VIP survey frames the issue around brands wanting visibility in AI-driven discovery channels — essentially, companies wanting their content surfaced by AI search systems and recommendation engines. That concern is legitimate and increasingly urgent as AI-based search reshapes where customers first learn about products. But the consumer-side findings cut against the optimism in that framing. If your audience is simultaneously skeptical of AI and put off by AI branding, you're operating in a tight space: present your product in AI-powered channels without triggering the distrust that the AI label itself creates.

For practitioners, the useful conclusion is not to abandon AI investment but to separate what you build from how you talk about it. Use the capability; drop the label when it doesn't add consumer value. The technology works without needing to be announced. Focusing on outcomes — faster, more accurate, customized to you — tends to persuade better than explaining the underlying process, whether that process is a simple rule engine or a sophisticated AI model.

The WordPress VIP data is a single survey, limited to the US, and should be read with appropriate caution. That said, the directional signal aligns with consumer sentiment research from the past two years. Enterprise AI spending will grow regardless; the real question is how fast consumer trust recovers and whether companies adjust their messaging before it damages their bottom line.