AI Recommendations Win Shopper Favor but Trust Gaps Remain, New Research Shows

Why It Matters

The research highlights a pivotal moment for the retail sector: AI can drive higher conversion rates and willingness to pay, but only if retailers address consumer concerns about manipulation and data accuracy. As Gen Z becomes a larger share of spenders, their comfort with AI could set new expectations for all age groups, forcing brands to rethink verification pathways and the role of human agents. Moreover, the findings signal that investment in composable commerce architectures is not just a technology upgrade but a strategic response to evolving shopper psychology. Retailers that fail to align AI capabilities with transparent, trustworthy experiences risk losing both sales and brand equity in a competitive market.

Key Takeaways

• 70% of ANZ shoppers demand personalized online experiences, yet 36% cite inaccurate product photos.
• 62% of ANZ consumers fear AI will push unnecessary purchases; 73% worry about AI replacing human service.
• 64% of Gen Z shoppers act on AI recommendations without third‑party verification; 95% do not cross‑check AI info.
• High shipping costs drive 70% of cart abandonment, underscoring the need for AI‑optimized delivery fee recommendations.
• Commercetools and Rithum plan new AI‑focused checkout and ethical guidelines to address trust gaps.

Pulse Analysis

The dual‑track insights from ANZ and Gen Z markets illustrate that AI adoption in retail is no longer a niche experiment but a mainstream expectation. Historically, retailers have leveraged personalization to boost basket size, but the current data shows a shift from optional add‑ons to core demand. The willingness of 56% of shoppers to pay more for exceptional experiences indicates that AI can be a revenue driver, provided it delivers tangible value without overstepping perceived boundaries.

However, the trust deficit revealed—particularly the 62% concern over AI‑driven persuasion—mirrors broader societal debates about algorithmic transparency. Retailers must therefore embed explainability into recommendation engines, allowing shoppers to see why a product is suggested. This could involve real‑time disclosure of data sources or confidence scores, which not only mitigates skepticism but also aligns with emerging regulatory expectations around AI ethics.

Finally, the generational divide suggests a phased approach to AI rollout. While Gen Z appears ready to hand over decision‑making to AI, older cohorts remain cautious. Retailers that adopt a tiered experience—offering AI‑enhanced shortcuts for younger shoppers while preserving traditional verification paths for others—will likely capture the widest market share. The upcoming composable commerce upgrades from commercetools and Rithum’s ethical guidelines could set industry standards, shaping how AI is integrated into the retail value chain for years to come.