The uncomfortable truths of the agentic commerce era

Last week OpenAI said it will start testing ads in ChatGPT. Within the hour, we had clients reaching out for our POV. And we love that: our clients are invested in optimizing every AI advancement, and we get excited about this stuff. But in the AI commerce world, there’s often a chasm between “announced” and “available.” In that in-between space, agencies pitch one thing, vendors promise another, and a whole lot of smoke-and-mirrors compete for your attention (and money). Meanwhile, there’s a black box of information from the actual AI platform.

In the near term, that uncertainty creates a very practical question for brands and retailers: what will it take to protect and grow AI share of voice in AI-driven shopping experiences as the rules change?

The entire ecommerce industry sees the same news about ads in OpenAI and gets excited. But no one truly knows what the buying model will look like (bidding? context targeting? something new?), what reporting will exist, or how the platforms will enforce relevance and transparency. 

What I, and others watching this announcement closely, do know is that ads inside an AI experience carry a unique risk: trust. In traditional search, users expect sponsored results. In an AI answer—where people are relying on the system to do the thinking—the separation between recommended and paid has to be unmistakable, or the whole experience will feel compromised. If ads are going to work in AI without breaking the product, they need to be tightly contextual—shown because they fit the answer, not instead of the answer. OpenAI has become a trusted source of information, and anything working against that, especially in commerce, erodes confidence in the platform itself.

Build AI readable product information now. 

Ads are the most recent example of how the mechanics and narrative of agentic commerce are being written week-by-week. Every part of agentic commerce is evolving in real-time, which creates constant pressure for brands and retailers to react just as quickly. When “someday” and “today” send mixed messages in bombastic headlines, how can you keep up?

The best way is to first recognize some of the uncomfortable truths, and what you can do to actually be ready for every stage of agentic commerce, whether it’s here or coming soon.  

Ads are the most recent example of how the mechanics and narrative of agentic commerce are being written week-by-week. Every part of agentic commerce is evolving in real-time, which creates constant pressure for brands and retailers to react just as quickly. When “someday” and “today” send mixed messages in bombastic headlines, how can you keep up?

You don’t need to predict the future to prepare for it. You just need to build the foundation that makes you recommendable, no matter what comes next. 

Another uncomfortable truth: right now, measuring AI share of voice is not a clean science.

The easiest version of AI visibility measurement is what many teams are doing today: you “put yourself into AI and see what comes back.” Run prompts, record results, compare competitors, and repeat.

That’s not wrong, but it’s also not perfect. Different people will see different answers. They’re typing different prompts, they’re in different locations, they phrase their prompts slightly differently, and the model may personalize based on prior behavior. When the platform itself is constantly changing, any number of variables can impact the true answer.

And if you buy a monitoring tool, there’s always a chance those variables just get wrapped in a prettier dashboard.  

It’s also difficult to scale. If you want product-level visibility, you’d need to test countless variations across categories, attributes, and intents. That becomes unwieldy fast, especially for large catalogs. And even if you spend the money to get those metrics, you still have to normalize the long-tail reality of prompts: three people ask the same thing in three different ways.

There’s no single “AI share of voice number” that behaves like a paid search impression share metric. And most brands aren’t directly feeding product data into large language models at scale yet. The ecosystem isn’t “plug in your catalog and win” (even if the pitch decks make it sound that way).

So, again, the path to visibility today is about teaching the AI to understand your product the way a real shopper evaluates it. Which leads to a practical directive: Make your product information crawlable, consistent, and verifiable.

The hardest truth: You don’t have an AI visibility problem. You have a PDP and trust problem.

If AI can’t confidently show you to your prospective customers, you don’t have an AI visibility problem. You have a truth and consistency problem across your PDPs, your marketplace listings, your imagery, your specs, your UGC footprint, and more.

If your data doesn’t match from one channel to the next, the AI sniffs out the inconsistencies, and it leaves you out of the answer. 

As the internet fills with content that AI recognizes as AI, then AI itself looks harder for authenticity.

If you’ve googled recently, you’ve seen Reddit going up higher in traditional search and even community opinions outranking polished brand websites. This on its own is a reflection of how AI is evolving: as the internet fills with content that AI recognizes as AI, then AI itself looks harder for authenticity. A forum thread, a review, a creator’s demo… these can’t be generated at scale by AI. And agentic commerce-based AI prioritizes authenticity over all else. Public conversations are now a material input into how AI recommendation systems form confidence. This is a huge hit to your brand voice.

A social post that says: “Buy the Jansport backpack for back-to-school” is brand-forward, but AI-invisible. A social post that says: “Get your water-resistant fabric, stain-resistant bottom, five compartment backpack that fits a 16” laptop and is overhead-bin friendly” is feature-forward and matches how people actually ask AI to shop.

In an agentic world, your brand name doesn’t bring the same level of trust. But strong features and PDPs mapped to the agentic commerce way of shopping create the new trust layer that AI looks for.

Trust is the new KPI. 

The agentic commerce era is uniquely exhausting. By the time you’ve finished reading the latest big AI announcement, the internet has already claimed that announcement is a new playbook. Even if that playbook is built on pure speculation.

Partial rollouts, unclear business models, lofty promises, and black-box systems have dropped the ecommerce industry into an uncomfortable, hazy ground of AI myths and truths. There are a lot of unknowns. But you can absolutely prepare for those unknowns in ways that will compound your readiness for wherever the industry advances.

Every layer of agentic advancement returns to a single concept: trust. 

Trust is why agentic shopping feels magical to consumers. 
Trust is why ads will look different than ever before. 
Trust is why UGC matters. 
Trust is why clean, consistent product truth is vital. 

In the agentic web, you have to earn inclusion. And inclusion is granted by systems that reward clarity, consistency, credibility, and proof.  

The headlines will keep shifting week by week. The black boxes will stay dark. But the flickers of guiding lights to follow shine from your clean data, consistent product truths, and a partner to help make sure you are staying at the cutting-edge. That is the best way to compound trust, and trust is what we know for a fact will always help you earn inclusion, even as the rules keep changing.