Zeta Global: AI-Driven Commerce: Reimagining Consumer Engagement and Brand Loyalty
Zeta Global — 2026 — Consumer Behavior
Zeta Global's May 2026 research examines how AI is transforming the consumer journey from discovery to checkout, delivering faster product discovery and personalized assistance while revealing significant demands for accuracy, transparency, and user control. The study finds widespread reliance on AI for efficiency accompanied by pervasive skepticism—most consumers cross-check AI outputs and many report encountering inaccurate AI-generated information, with younger cohorts both leading adoption and applying stricter verification. It recommends that brands invest in first-party data, proprietary AI experiences, rigorous governance and human-in-the-loop processes to restore trust, strengthen direct customer relationships, and translate AI-enabled efficiency into sustained loyalty and measurable commercial outcomes.
Key Statistics
- 36% of AI shoppers report reduced time spent on product research due to AI-assisted shopping.
- 95% of AI shoppers cross-check AI recommendations before making a purchase.
- 44% of AI shoppers have encountered inaccurate AI-generated shopping information.
- 41% of younger consumers are willing to let AI make purchases within a set budget, compared to 27% of older consumers.
- 47% of younger consumers always fact-check AI information before buying, versus 26% of consumers over 45.
Key Takeaways
- Invest in first-party data infrastructure (e.g., a Customer Data Platform) to consolidate customer profiles and enable proprietary, trustworthy AI experiences.
- Develop brand-specific AI tools (recommendation engines, conversational assistants, product configurators) on owned channels to preserve differentiation and direct customer relationships.
- Implement strict data governance and accuracy checks, including audit trails and attribution for AI-sourced information, to reduce misinformation and rebuild trust.
- Design human-in-the-loop workflows and escalation thresholds (e.g., route to live agents when AI confidence is low) to manage sensitive interactions and minimize errors.
- Enhance loyalty programs with AI-driven personalization and tiered rewards based on behavioral and lifecycle data to counteract discovery-driven erosion of brand loyalty.