A shopper asks an AI assistant to find a portable speaker under $150 that can arrive before the weekend. Product quality matters. Reviews matter. Price matters. But so does something marketers have traditionally considered further down the purchase journey: whether the product is actually available when the consumer wants it.
That distinction is becoming more important as AI-assisted shopping moves from product search towards product consideration. Amazon says Rufus was used by more than 300 million customers in 2025 and helped generate nearly $12 billion in incremental annualised sales. The significance for marketers is not simply that consumers have another discovery interface. AI can increasingly evaluate a product alongside the circumstances that make it useful to a particular shopper.
This could expand what brands consider a marketing signal.
AI Is Moving Closer to the Consideration Decision
Retailer-owned assistants such as Amazon Rufus and Walmart Sparky are designed to do more than retrieve products. They can compare options, summarise reviews and help consumers narrow choices around specific needs.
That does not mean shoppers are ready to hand over the entire purchase decision. Gartner’s 2026 research found that only 11% of US consumers were willing to let AI make purchase decisions, but considerably more were comfortable allowing it to narrow their choices – 31% for household supplies and 28% for personal electronics.
The immediate opportunity is less about AI buying on behalf of consumers and more about AI influencing which products make it into consideration in the first place. And as the questions consumers ask become more specific, the information required to answer them becomes richer.
A request for the “best headphones” is largely about product relevance. The “best headphones under $200 that can arrive tomorrow” introduces price, inventory and fulfilment into the same decision.
Product Information Is Only the Starting Point
Brands have invested heavily in strengthening the digital shelf through product descriptions, imagery, specifications, ratings and reviews. Those fundamentals remain important in AI-assisted shopping.
But emerging AI commerce infrastructure shows that these systems can draw on a wider set of information. Shopify’s agentic-commerce infrastructure, for example, can make product descriptions, images, pricing, inventory and shipping information available to connected AI platforms.
Consumer priorities help explain why these signals matter. Visa’s 2026 research found that price is an important consideration for 77% of online shoppers, while delivery speed matters to 66%.
Relevance is therefore becoming more situational. The right product also needs to make sense for the shopper’s immediate circumstances.
When Availability Becomes Part of Visibility
Consider two products that meet the same need. One may have stronger brand recognition; another may be available nearby and able to arrive tomorrow.
Which is the better recommendation depends on what the shopper is asking.
Availability, then, has the potential to become more than a fulfilment consideration. It can become an input into whether a product is useful to recommend at that particular moment.
This does not mean inventory or delivery speed should be treated as universal AI ranking factors. Shopping assistants operate differently, and their recommendation systems will continue to evolve. A more useful implication is that AI-assisted shopping is bringing demand creation and demand fulfilment closer together. For brands, that means some of the signals traditionally associated with completing a purchase can increasingly influence the consideration that precedes it.
Marketing Doesn’t Need to Own Inventory. It Needs Visibility Into It.
This does not turn inventory management into a marketing responsibility. It does make coordination between marketing, commerce and retail teams more consequential.
A brand may create strong demand for a priority product, but availability determines whether that demand can be converted in a particular market or moment. Conversely, strong availability and fulfilment can become more valuable when marketing knows where those advantages can support consumer consideration.
For CMOs, this creates a different set of questions. Are the products receiving the greatest marketing support consistently available where demand is being created? Can AI systems understand the full proposition around them, including price and fulfilment? And are commerce signals informing where retail media investment can work hardest?
These are not questions for marketing to answer alone. They point to a closer connection between marketing, commerce and retail teams as the signals influencing consideration expand.
The objective is not to optimise every operational signal for an algorithm. It is to ensure that the promise marketing creates and the proposition commerce can deliver remain connected.
Relevance Is Becoming More Situational
AI-assisted shopping does not diminish the importance of brand, creative or product differentiation. It adds another layer of context to them.
Marketing has traditionally focused on making a product worth wanting. AI-assisted shopping adds another dimension: whether that product is the best available answer when the consumer is ready to act.
That makes availability more than an operational condition. Increasingly, it can become part of a brand’s ability to convert relevance into consideration.
