Tiger Tracks · Eye of the Tiger · Creative and Content · October 2026

Consumer concern about AI is rising, not fading. In a June 2025 Pew Research Center survey of 5,023 US adults, 50% said they were more concerned than excited about the increased use of AI in daily life, up from 37% in 2021 [1]. A 47-country study by the University of Melbourne and KPMG found the same tension at global scale: 66% of people use AI regularly, yet only 46% are willing to trust it [2].

Brands leveraging AI therefore face a dual challenge: using advanced tools to deepen engagement while ensuring that trust is never compromised. This article explores how AI intersects with consumer trust, and what brands must manage to cross the trust threshold authentically.

1. The Trust Threshold Sets the Terms of Engagement

The trust threshold is the point at which consumers feel comfortable engaging with a brand's AI-powered content and experiences. Crossing it builds loyalty; missing it risks alienation.

The gap between use and trust is where the threshold sits. People rely on AI daily, and in the KPMG study 83% expected AI to deliver a wide range of benefits, yet fewer than half were willing to trust it and 70% said AI regulation is needed [2]. AI can amplify trust by delivering relevant, respectful interactions, but it can just as easily backfire if content feels artificial or intrusive.

2. Privacy Is the Baseline, Not a Feature

Consumers expect brands to protect their data and use it responsibly, and trust is earned through consistent respect for those boundaries. In Cisco's 2024 survey of 2,600 consumers in 12 countries, 75% said they would not purchase from an organization they do not trust with their data [3]. AI raises the stakes: 84% of respondents were concerned to some degree that data entered into generative AI tools could be shared, and 59% said strong privacy laws make them more comfortable sharing information in AI applications [3].

Micro-Moments of Trust

AI enables brands to identify and act on micro-moments: small, context-rich interactions that build incremental trust. Personalized recommendations or timely customer support powered by AI can create positive, trust-enhancing experiences when the data behind them was shared knowingly.

Privacy Versus Personalization

3. AI-Generated Content Can Fall Into the Uncanny Valley

Content that feels "off" or unnatural triggers discomfort, and the effect is measurable. In a NielsenIQ study of more than 2,000 participants, about 150 of them monitored by EEG, consumers intuitively identified most AI-generated ads, described them as "annoying," "boring" and "confusing," and showed weaker memory activation even for ads perceived as high quality [4]. NielsenIQ also reported a negative brand halo, in which skepticism about the ad carried over to the brand [4].

Detection cuts both ways. While 76% of Americans want to be able to tell AI content from human content, 53% are not confident they can [1], which means suspicion can attach to work that was never machine-made.

Signs of Uncanny AI Content

• Overly generic or repetitive messaging
• Tone that feels robotic or insincere
• Content that misses cultural or emotional nuance
• Poor visual quality that forces extra effort to process the message [4]

Strategies to Bridge the Gap

• Integrate human oversight and creativity into AI workflows
• Use AI to enhance, not replace, authentic brand voice
• Test content with real audiences before broad deployment

Overreliance on AI-generated content risks eroding brand authenticity and consumer trust, and the damage can extend from a single ad to the brand itself [4].

4. Disclosure Is Becoming a Requirement

Advertisers consistently overestimate how audiences feel about AI. In January 2026, IAB reported that 82% of advertising executives believe Gen Z and Millennial consumers view AI ads positively, while only 45% of those consumers do, a gap that widened from 32 points in 2024 to 37 points [5]. The same research found that 73% of Gen Z and Millennials say clear disclosure would increase or maintain their likelihood to purchase [5].

IAB's response was the industry's first AI Transparency and Disclosure Framework, a risk-based model that pairs consumer-facing labels with machine-readable C2PA metadata where AI materially affects authenticity, identity or representation, such as synthetic humans and digital twins [5]. Regulation now points the same way. The transparency obligations in Article 50 of the EU AI Act, including disclosure for AI interactions and labeling of deepfakes, have applied since August 2, 2026; generative AI systems already on the market before that date have until December 2, 2026 to meet the machine-readable marking requirement [6].

5. Creative Scale Must Not Cost Authenticity

AI offers unprecedented scale to personalize and diversify content. Scaling creativity, however, requires maintaining authenticity at every touchpoint.

Balancing Scale and Authenticity

Brands must combine AI efficiency with human empathy. This hybrid approach allows scaling of personalized experiences while preserving genuine brand-consumer connections.

Practical Applications

• Dynamic content that adapts to user behavior in real time
• Automated yet personalized customer service interactions, with clear disclosure when a customer is talking to AI
• AI-driven insights guiding human creative decisions

The practical rule is simple: use AI-generated data to inform creative strategy, not to fully automate creative output.

Conclusion

Trust is the currency of consumer relationships in the AI era, and the data shows consumers are spending it carefully. Brands that prioritize privacy, disclose their use of AI plainly and keep a recognizable voice will cross the trust threshold; brands that treat AI as a shortcut will find the threshold moving away from them. The future belongs to those who balance AI's creative power with human judgment. That is the Human-Led, AI-Augmented advantage.

References

1. Pew Research Center. (September 17, 2025). How Americans View AI and Its Impact on People and Society. https://www.pewresearch.org/science/2025/09/17/how-americans-view-ai-and-its-impact-on-people-and-society/
2. Gillespie, N., Lockey, S., et al., University of Melbourne and KPMG. (May 2025). Trust, attitudes and use of artificial intelligence: A global study 2025. https://kpmg.com/ee/en/insights/2025/05/Trust-attitudes-and-use-of-artificial-intelligence-A-global-study-2025.html
3. Cisco. (October 30, 2024). Cisco 2024 Consumer Privacy Survey. https://bxin.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-consumer-privacy-report-2024.pdf
4. NielsenIQ. (December 12, 2024). NIQ Research Uncovers Hidden Consumer Attitudes Toward AI-Generated Ads. https://nielseniq.com/global/en/news-center/2024/niq-research-uncovers-hidden-consumer-attitudes-toward-ai-generated-ads/
5. IAB. (January 15, 2026). IAB Releases Industry's First AI Transparency and Disclosure Framework to Guide Responsible Advertising in a Generative-AI Landscape. https://www.iab.com/news/iab-releases-industrys-first-ai-transparency-and-disclosure-framework-to-guide-responsible-advertising-in-a-generative-ai-landscape/
6. Machin, E., Ropes & Gray. (August 3, 2026). You Talkin' To Me? Operationalising The EU AI Act's Transparency Obligations. https://www.ropesgray.com/en/insights/viewpoints/2026/08/102nfqm/you-talkin-to-me-operationalising-the-eu-ai-acts-transparency-obligations

Published by Tiger Tracks. Eye of the Tiger Intelligence Series.