Generative AI is changing how people discover, evaluate, and buy products. The buyer journey is compressing, driven by faster answers, fewer clicks, and increasingly automated decision-making. As this shift accelerates, one question becomes critical: what signals of trust still matter when the storefront disappears?

One of the strongest signals these models are likely to rely on is authentic customer reviews. Not because they’re perfect, but because they are trusted by shoppers and provide true insight into shoppers’ experiences. Public and abundant, reviews exist across product pages, marketplaces, social platforms, and forums. They’re also dynamic and continuously updated by the most recent customer experiences in real time. That ubiquity and relevancy make them a natural source of data for large language models trained to detect patterns, surface insights, and summarize product feedback at scale.

The Role of Reviews in Agentic Commerce

AI systems like ChatGPT are increasingly acting as buying agents, responding to prompts like “best value kitchen blender under $100” or “most reliable sunscreen for sensitive skin.” These queries often include qualifying language (e.g. best, most durable, highest rated, cheapest) that requires nuance and context to answer. Tomorrow’s AI systems won’t just index product catalogs; they’ll interpret and summarize the content they can access. Reviews, especially in aggregate, offer the kind of lived-experience context these systems need to respond well.

Unlike static marketing copy, reviews are dynamic, unstructured but interpretable, and reflective of real-world usage. When hundreds of customers reinforce a theme (e.g. “this stroller folds flat,” “this tent leaked,” “this battery lasts longer than expected”) AI can pick up on that signal and incorporate it into its responses.   

They won’t be the only input. But reviews are one of the few signals that are both abundant and behaviorally aligned with how AI parses user intent. 

The Growing Importance of Authenticity

Shoppers are skeptical of reviews that feel artificial. If your reviews read like boilerplate or PR, they may be doing more harm than good, especially in contexts where the shopper can’t see the product or browse alternatives. Even when the content is accurate, consumers are less likely to trust reviews they believe were written by AI. They rate those reviews as lower in credibility, helpfulness, and authenticity (Amos and Zhang, 2024).

In a 2024 study, both humans and AI language models were tasked with distinguishing real reviews from AI-generated ones. Accuracy hovered around 50% (i.e. no better than chance) (Carter, Forrest, et. al, 2025) . If models can’t distinguish real from fake, they have to rely on deeper signals: specificity, tone, verified purchase status, linguistic patterns and proof of humanity. As generative commerce grows, AI systems will evolve better tools for detecting synthetic content. At the same time, retailers, marketplaces, and regulatory bodies will put more pressure on brands to prove authenticity through verified purchase signals, transparent collection methods, and stricter moderation practices.

What a Scalable Authenticity Strategy Looks Like

Authenticity doesn’t just happen. It’s built through consistent systems, intentional design, and a clear understanding of what today’s platforms (and shoppers) expect from review content.

The goal isn’t to manufacture praise. It’s to make it easy for real customers to speak credibly about real experiences and to preserve that voice across every surface where your products are discovered.

Here’s what that looks like in practice:

1. Systematic collection from real customers

The most effective review strategies start at the moment of transaction — not weeks later. Whether it’s an email prompt post-purchase, a QR code on-pack, or an SMS follow-up, your systems should make it easy (and expected) for customers to leave feedback. 

Consistency builds volume. And volume — when it’s real — builds trust.

2. Prompts that drive specific, experience-based responses

Generic asks get generic answers. But with thoughtful, targeted prompts — “How did this product perform under stress?” or “Where did you take it?” — you surface the kind of detail that makes a review feel real and helpful.

It also aligns your review content with how AI parses user intent. Shoppers don’t ask “What’s a good cooler?” They ask “What’s the best cooler for long road trips?” You want reviews that speak to those use cases.

3. Moderation and authentication that meets compliance — and builds trust

The FTC is cracking down on fake or manipulated reviews, and marketplaces are responding with stricter standards. You need a review pipeline that includes:

1. Verified purchase tracking 

2. Incentive transparency 

3. AI-assisted moderation for risky or non-compliant content 

4. Syndication that preserves authenticity across retail environments 

Too many legacy systems treat reviews like leased content: if you cancel, they vanish. Or worse, they’re scrubbed of metadata, incentives, or context when they’re syndicated across retail.

Modern review syndication prioritizes permanence, portability, and transparency. Your authentic content should follow your products — and retain the structure and signals that make it useful to both shoppers and AI. 

Preparing for AI with Authentic Human Feedback

As AI models become more discerning and platforms become more regulated, credibility and recency will beat volume. And brands that can consistently generate and syndicate authentic, experience-based feedback will earn the trust that drives visibility and conversion. The systems you build now will determine how visible and trusted your products are in the commerce experiences of the future, but only if you treat reviews as infrastructure, not a checkbox.

Sources:

Amos, Clinton, and Lixuan Zhang. “Consumer reactions to perceived undisclosed chatgpt usage in an online review context.” Telematics and Informatics, vol. 93, Sept. 2024, p. 102163, https://doi.org/10.1016/j.tele.2024.102163.

Carter, C.J, Forrest, Mina et. al, "Large language models as 'hidden persuaders': Fake Product Reviews are Indistinguishable to humans and machines." Computation and Language, Jun. 2025.