Commerce AI has a measurement problem no one is talking about

Presented by Rezolve Ai


Most brands know something is shifting in how consumers find and choose products. What most don’t know is how much of that shift has already taken place, where it’s happening, or whether they’re on the right side of it. That uncertainty is the problem. And the analytics stack most brands rely on isn’t built to resolve it.

The decision layer has moved

In 2014, 82% of digital commerce started on a brand’s website. By 2024 that had fallen to 38%, according to Salesforce research. The journey that used to begin at a brand’s front door now begins somewhere else. Increasingly, it begins with a question asked of an AI platform and ends with an answer that shapes the purchase decision before any brand-owned touchpoint is engaged.

Consumers are asking AI where to shop, what to buy, and which product is right for them. Bain research shows that four in five consumers rely on zero-click results at least 40% of the time. That means the shortlist a consumer receives from an AI answer engine is, in many cases, the only shortlist they consult. Adobe Analytics recorded over 800% year-over-year growth in AI-driven traffic to retail sites, a signal of how rapidly AI platforms are inserting themselves between brands and their customers.

This is a structural shift, not a trend. And it has created a category of commercial loss that most analytics tools are architecturally incapable of detecting.

What you can’t see is costing you

The gap is this: a brand can have strong onsite conversion metrics and still be losing significant ground in the market, because the customers who never arrived aren’t captured in any dashboard. There’s no “AI excluded you” event in a session log. There’s no abandoned cart entry for a shopper who was told by an AI assistant that a competitor was the better fit.

This is different from the SEO problem brands have managed for two decades. With traditional search, absence had a visible signal. You could see your ranking, audit the gap, and act on it. With AI answer engines, absence is invisible by default. The surface doesn’t show you what it didn’t show the consumer.

Sixty percent of searches now end without a click, according to Semrush’s 2025 zero-click study. For AI-mediated discovery, that number is structurally higher. The answer is the destination. If a brand isn’t in the answer, it isn’t in the consideration set, and its analytics will never surface that fact.

The metric that isn’t being measured

The commerce industry has developed sophisticated instrumentation for the journey from landing page to purchase. It has essentially no instrumentation for the journey from consumer intent to brand discovery, the layer where AI is now operating.

Brands that want to understand their actual competitive position in an AI-mediated market need to ask a different set of questions: How does my brand appear when consumers ask AI for recommendations in my category? What language does AI use to describe my products? Where am I present, where am I absent, and where am I being described in ways that don’t reflect my positioning?

These aren’t marketing questions. They’re infrastructure questions. And answering them requires a different kind of audit than anything in the current commerce or marketing toolkit.

Rezolve Ai commissioned research across 1,500 US consumers in January 2025 that found the majority of shoppers who use AI for product research make purchase decisions directly from those AI-generated recommendations, without returning to a search engine or brand site to verify. The implication for brands is significant: by the time a consumer reaches a brand’s owned properties, the decision may already have been made, or unmade, somewhere else.

What comes next

The brands that will maintain commercial relevance as AI mediates more of the discovery layer are those that develop visibility into it, not just presence on their own platforms. That means treating AI discoverability as a measurable discipline, not an assumption, and building the infrastructure to understand, track, and influence how AI systems represent them to consumers.

The tools to do that are emerging. The measurement frameworks are not yet standardized. But the brands that begin building that visibility now will have a structural advantage as the market continues to shift.

AI answer engines are already forming preferences. Every day without visibility is a day those preferences solidify without you.


Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

The Next AI Challenge For Enterprises: Pricing Intelligence, Not Just Model Intelligence

As enterprise AI scales, success will depend on matching model capability, cost and business value to every task.

The Cost Of Inaction: Why Businesses Can’t Afford To Ignore Modernization

The modern and advanced technologies that once felt out of reach for SMBs are now more accessible than ever before, giving these organizations a clear path to modernizing and embracing the change that will come with that process.​

The CapEx Trap: Why Healthcare Is Bleeding Capital On Legacy Systems And Garage-Built Toys

Healthcare AI is stuck between white-washed legacy systems and costly garage-built tools. Here’s why health systems need agentic “patient capture engines” built for execution, not conversation.

Why Build Vs. Buy Is The Wrong Question For Healthcare AI

Every abandoned build makes the next one harder to launch because clinicians have learned to expect abandonment.

AI Has Made Building Faster Than Fundraising

With AI, one person can sketch the concept, generate copy, create a landing page, prototype a workflow, test messaging, analyze competitors and build a workable MVP in the middle of the night.

Plugable’s New USB-C Dock Solves Triple-Monitor Headache Without Drivers

Computer accessory brand Plugable has announced a premium USB-C dock designed for up to three displays using MST Technology. No drivers needed with WIndows and Chrome.

​Your NPS Score Is Not The Problem—Your NPS Program Is

The problem isn’t NPS itself. It’s how most organizations run their NPS programs, and how they interpret and use the resulting score.​

Adding Cost To Tokens Per Watt Is The New Metric For AI Productivity

“Tokenmaxxing” is a Silicon Valley workplace trend where employees maximize their AI token consumption to demonstrate productivity.

The Translator Gap Is Widening, And Closing It Is A Leadership Job

Someone has to translate the business expectation into technical reality, and then translate the technical reality back into business consequences.