The news: AI users don’t trust platforms’ use of their data and are defending their privacy and personal information.

• 59% of US adults don’t feel comfortable with AI using their emails to personalize ads, and 52% are uncomfortable sharing their browsing history, per Cloaked’s How Americans Feel About Sharing Their Data With AI report.
• Over half (53%) have opted out of data collection, targeting, or tracking ads on AI platforms.
• Another 31% have given an AI platform a fake name or birthday.

The challenge: Data quality is eroding at the source—if users are giving fake answers to basic inputs or holding back on behavioral data, personalization models degrade.

Marketers who assume they have full visibility into a user will be working off of a distorted picture if someone allowed tracking but input misleading signals.

• This creates a false sense of precision in targeting and measurement, which is a problem given the high premium consumers place on personalized AI experiences.
• That sets up a ceiling on hyper-personalization: The more invasive customization feels, the more users subtly push back, creating a self-limiting loop for AI-driven ad strategies.

The opportunity: First-party relationships matter more than ever when users are selectively honest, as consumers are more likely to share accurate data in contexts they trust—like logged-in experiences and subscription accounts—versus open platforms.

• Trust is becoming a gatekeeper for data accuracy, meaning brands without strong direct relationships will see greater signal loss.
• Consumers’ distrust also shifts competitive advantage toward companies that can turn anonymous users into authenticated ones.

Recommendations for brands: Frame AI personalization features as a value exchange and treat data as directional information, not the absolute truth.

• Clarify the benefits of data sharing for customized AI responses and recommendations and provide transparent policies.
• Reduce reliance on hyper-granular targeting to avoid overfitting to highly specific—but potentially false—markers.

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