For marketers, generative AI promises faster production, lower costs and an almost limitless supply of creative variations. But a new study from Rival Technologies suggests that efficiency can carry a consumer cost—particularly among Gen Z. The research found that 72% of surveyed Gen Z consumers had taken some form of negative action after encountering marketing they knew was AI-generated, raising questions about whether brands are moving faster with AI than their youngest customers are prepared to accept.
Generative AI has quickly become part of the advertising production pipeline. Brands can now generate campaign concepts, social posts, images, video and personalized creative at a fraction of the time traditional production can require.
But consumers do not necessarily see that efficiency as a benefit.
A new study from Rival Technologies, based on 901 Gen Z participants in the U.S. and Canada, suggests that AI-generated marketing can trigger a meaningful behavioral response among younger consumers. According to the company’s July 2026 research, 74% of respondents reacted negatively when they realized a brand’s marketing had been created with AI, while only 8% reacted positively.
More importantly for marketers, the response was not confined to sentiment.
Rival reports that 50% of respondents had unfollowed a brand after encountering AI-generated marketing, 48% had unsubscribed from emails or text messages, 49% had complained to friends, family or online, and 43% said they had stopped buying from a brand.
Those figures come from Rival’s own research and should therefore be interpreted as survey findings rather than a market-wide measure of consumer behavior. Still, they align with a broader body of research suggesting that consumers can be more skeptical of AI-generated advertising than marketers expect.
The Interactive Advertising Bureau, in research published in January 2026, found that Gen Z and millennial consumers continued to feel less positive about AI-generated advertising than advertising executives anticipated. The IAB also found that disclosure of AI use could improve consumer receptiveness.
That gap between advertiser expectations and consumer reaction is becoming one of the more important issues in AI marketing.
The problem isn’t necessarily the pixels
Rival’s findings offer an interesting explanation for the backlash.
The strongest objections were reportedly tied less to whether AI-generated content looked convincing and more to what respondents believed AI meant for people. Concerns included job displacement and the impact of generative AI on artists and creative workers.
That distinction matters because it changes the marketing challenge.
A technically impressive AI-generated advertisement can still be perceived negatively if consumers believe the company used AI primarily to eliminate human creative work. The question becomes less “Does this ad look good?” and more “Why did this company choose to make it this way?”
Recent academic research points in a similar direction. A 2026 Journal of Business Research study found across eight studies that disclosure of AI use in digital advertising reduced engagement in the environments tested, partly because consumers perceived AI-generated marketing as requiring less effort and associated the output with lower product quality.
Another 2026 study examining more than 1.3 million Reddit posts found that public discussion around generative AI has increasingly shifted toward socioeconomic and ethical concerns, rather than focusing exclusively on technical capabilities.
For marketers, this suggests that AI adoption is not simply a creative-production decision. It is increasingly a brand positioning decision.
Disclosure alone may not solve the problem
The emerging instinct among brands has been to disclose AI involvement more clearly. That is an important step, but transparency does not necessarily eliminate consumer objections.
The IAB’s 2026 research suggests disclosure can improve purchase likelihood among young consumers, indicating that consumers may respond better when brands are open about AI use rather than allowing audiences to discover it themselves.
But transparency and acceptance are different things.
A disclosure that says an image was generated with AI may answer how the content was produced without answering why the brand chose AI, what role human creatives played or whether consumers’ concerns about employment and authenticity have been considered.
That is particularly relevant for Gen Z, whose attitudes are not necessarily uniform.
Rival found that strong negative reactions increased with age within its Gen Z sample, rising from 44% among respondents aged 18–20 to 54% among those aged 25–29. The company also reported a substantial geographic difference: 84% of Canadian respondents reacted negatively compared with 65% in the U.S.
Those differences are a reminder that “Gen Z” is not a single consumer segment. Cultural context, age, category, familiarity with AI and the type of marketing involved can all influence perception.
Research on AI-driven personalized advertising among Indian Gen Z consumers published in Frontiers in Communication in June 2026 similarly found concerns around privacy, consent, data ownership and regulatory control. The study was qualitative and involved 20 participants, so it should not be compared directly with Rival’s larger survey, but both point toward a broader issue: younger consumers increasingly want greater control and transparency around how AI is used in marketing.
The implications for marketing teams
The findings do not mean brands should stop using AI.
AI is already becoming embedded across creative production, media buying, personalization, customer analytics and marketing operations. The competitive question for companies such as Google, Meta, Adobe, Salesforce and Microsoft is increasingly how to make AI useful without making the customer experience feel automated or impersonal.
For marketing teams, the more practical lesson is to distinguish between using AI behind the scenes and making AI the visible proposition.
AI can help a team analyze customer research, identify audience segments, generate creative variants or optimize campaign delivery without necessarily becoming the central message of the campaign.
When consumers can see that AI has created the advertisement, however, brands may need to provide more context. Human creative direction, original photography, artists, designers and subject-matter experts can remain important signals of authenticity even when AI is part of the workflow.
This becomes particularly important when a brand’s AI strategy is communicated alongside layoffs or reductions in creative staffing. As Rival’s research suggests, a message celebrating AI efficiency could resonate differently if consumers interpret that efficiency as evidence that people were displaced to produce the campaign.
There is also a strategic risk in assuming that AI-generated content will become universally accepted simply because consumers become more accustomed to it.
The market may instead split.
Some audiences may value inexpensive, personalized and rapidly generated content. Others may increasingly treat visible AI involvement as a negative quality or authenticity signal. Brands operating across categories and markets will need to determine where their customers sit on that spectrum.
AI marketing enters an authenticity test
The first phase of AI marketing was largely about proving what machines could produce.
The next phase may be about proving that brands know when not to use them.
Rival’s study adds to growing evidence that consumers are evaluating AI marketing through questions of authenticity, labor, transparency and trust—not merely visual quality.
For marketers, that means the best AI strategy may not be maximum automation. It may be selective automation, with humans remaining visible where creativity, cultural judgment and emotional credibility matter most.
AI can make marketing faster. The harder task is making sure consumers do not interpret that speed as a reason to trust the brand less.
Market Landscape
AI-generated advertising is moving rapidly from experimentation into mainstream marketing workflows, but consumer acceptance remains uneven.
The IAB’s January 2026 research found a continuing disconnect between advertising executives and younger consumers: brands are increasing their use of AI in advertising while Gen Z and millennials remain less positive about AI-generated ads than industry executives expect. The research also found that disclosure can improve consumer receptiveness.
That creates a new tension for the advertising ecosystem. AI can reduce production costs and accelerate creative testing, while consumers may interpret visible AI use as a signal of lower effort, reduced authenticity or weaker human involvement.
The competitive landscape spans the entire marketing stack. Google and Meta are integrating AI into advertising platforms; Adobe and Salesforce are embedding AI into creative, CRM and marketing workflows; and agencies are experimenting with AI-assisted production and media optimization.
For brands, the strategic question is shifting from whether to use AI to where AI should be visible.
The strongest applications may remain those where AI improves the machinery behind marketing while humans retain responsibility for brand voice, cultural interpretation and creative judgment.
Top Insights
- Rival Technologies reports that 72% of surveyed Gen Z consumers took negative action after encountering AI-generated marketing, raising concerns for brand retention.
- The study found negative sentiment was tied to concerns about jobs, artists and human creativity, suggesting AI marketing can become a values issue.
- IAB research similarly finds younger consumers are less enthusiastic about AI-generated advertising than marketers expect, although disclosure can improve receptiveness.
- Marketers may need to distinguish between AI used behind the scenes and highly visible AI-generated creative, where authenticity and perceived effort influence consumer response.
- Enterprise brands adopting generative AI will increasingly need governance covering disclosure, creative quality, workforce implications, data practices and audience trust.
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