How AI Is Reshaping Ecommerce Discovery, SEO and Marketing Attribution

1. What shifts are you observing in ecommerce traffic and consumer behaviour, and how do these differ from the traditional search journey?

The clearest visible shift is that ecommerce site visits are growing faster than unique visitors. Over the last twelve months, worldwide web visits grew 6.8% against 5.8% for visitors. The year before, the pattern ran the other way (+1.3% visits, +4.5% visitors). The effect is sharper in some markets. In the UK, visits grew while unique visitors fell; fewer people are arriving, but they are browsing more intently.

AI is not the sole cause, as consumers are doing more research before committing generally, but it feeds directly into that behaviour. Nearly half of US consumers (49.1%) say AI has changed how they browse, and AI’s presence in journeys that end at a transactional retail site has risen from 4.5% to 11.4% in under two years.

The difference from a traditional search journey is friction. AI lets a shopper shortlist, compare and get tailored advice in one conversation, in what feels like a neutral environment. Previously that meant going back and forth between retailer sites, buying guides and affiliate content, where the impartiality of the advice was often in question.

2.  What does shorter discovery-to-purchase mean for retailers that relied on multiple visits, product-page engagement and retargeting?

AI may shorten the path, but it does not reduce engagement overall. Journeys that combine AI and Search span an average of 14.9 sites, against 10.1 for Search alone. AI-only journeys are shorter at 7.3 sites, but they account for just 11% of journeys that use AI. What changes is where the engagement happens. Shoppers may reach a retailer’s site directly only at the final purchase, which moves much of the relationship away from the retailer.

That does not make retargeting ineffective. The shopper may simply use AI to research the very products you retarget them with. The retailer no longer owns the whole journey, but it still owns the product details, pricing, fulfilment options and any existing customer relationship that drive the final purchase and repeat business.

3 & 4. How should ecommerce businesses understand the relationship between AI discovery and search, and what does it mean for SEO and paid strategy?

They should not treat them as separate channels. In 89% of journeys where AI appears, Search is present too. Consumers are stacking tools, not switching between them. AI sits disproportionately in the middle of the journey, appearing as a middle touchpoint in 76% of the journeys where it is used, against 23% as the first touchpoint and 18% as the last. Search is spread far more evenly, appearing at the start, middle and end of at least half of the journeys where it is used. The most complex journeys, those using both, convert best. AI is a new touchpoint, not a replacement destination.

Established SEO and paid practices remain effective. Each channel still needs its own focus, but they should feed one strategy. The question is no longer “how are we performing in SEO and paid?” but “what is the combined result of our efforts across AI visibility, search and paid?” Retailers should rethink the following:

●     Product data: detailed enough for an AI tool to reason over, with descriptions built for both humans and LLMs.

●     Brand consistency: the same story across paid social, Google and the retailer’s own site, so it holds whichever tool a shopper reaches for.

●     Marketplace-native assistants: conversational assistants such as Amazon’s Alexa for Shopping are growing faster than external AI referrals. Traffic to Amazon product pages from the assistant was up 537% year on year in June 2026, against +203% for external Gen AI referrals to ecommerce sites. They apply their own rules on who gets seen, favouring sponsored placement and purchase history, which traditional SEO logic doesn’t reach.

5. How does AI influence before the site visit complicate attribution, and are existing models equipped for it?

No, they are not. Traditional attribution significantly undercounts AI’s impact, and indirect influence has always been an issue. A product seen on social that leads to a branded Google search shows up as a search referral, for example. The misattribution is greatest for AI because shoppers use it to discover, evaluate and research, but rarely to navigate. They tend to take what they learned and then search for the brand or type the URL.

The scale of the gap is visible in our data. Gen AI referrals account for under 1% of worldwide ecommerce web visits, yet AI appears in 11.4% of US desktop journeys that end at a transactional site. Looking only at referrals from AI platforms measures the smallest and least representative part of AI’s role, and is the wrong metric.

6. What data should ecommerce leaders combine to understand the impact of AI shopping?

No single dataset will answer this, so leaders should combine four views:

●     AI visibility: how often and how prominently the brand appears in AI responses for relevant prompts, and which sources those responses cite.

●     Prompt intent and category influence: which types of prompts (research, comparison, transactional) and which topics within your category AI influences most (think Skin Care within Beauty).

●     Referral and traffic data: AI referrals,where the growth rate is a better gauge of adoption than the share, alongside direct and branded search trends that may reflect AI-influenced demand.

●     Journey-level conversion: how conversion differs between AI-only, Search-only and combined journeys.

Together these show not just how many visitors AI sends, but where it shapes the decision.

7. Does AI discovery translate into higher purchase intent, or does it just shift where consumers research?

Purchase intent may be the wrong question. It’s unlikely any channel or research tool creates intent. What AI does is increase confidence in a decision, and that is its greatest impact. It shows most clearly in complex journeys involving both AI and Search, where

23.0% of sessions that include a transactional site end in conversion, compared with 17.3% for Search alone and 12.5% for AI alone.

This is an association rather than proof of causation, but it fits the confidence argument: shoppers who combine AI and Search are cross-checking sources before they commit. To separate browsing from meaningful buying intent, retailers should look at conversion by journey type, not just AI-referred traffic volume.

8. What should retailers change first: product information, pricing, onsite experience or acquisition strategy?

Product information first, then pricing.

●     Product information: complete, clear data is what lets AI models make full comparisons, and it is the one lever the retailer fully controls.

●     Pricing: a shopper may ask a final question about where to buy at the best price. If a competitor is cheaper, the sale could be lost after all the preceding work.

●     Acquisition strategy: this isn’t a step away from acquisition. It is a strategy for acquiring AI-assisted shoppers.

●     Onsite experience: it perhaps matters least of the four, because if an LLM takes the shopper straight to the product, the wider site had little to do. The exception is checkout. After all the groundwork to get a shopper to that point, a single friction point can undo it.

9. What is the biggest assumption marketing leaders will need to revisit by 2027, and what data should inform it?

The biggest assumption is that the channels you can measure are the channels that matter. Many leaders still treat AI as a separate channel, judged by referral volume, and set it against search as a competing channel while the evidence points the other way. AI shows up in more than one in ten journeys to a transactional site, almost always alongside Search, and mainly works through the middle of the path. Its role is also broadening: its share as a middle touchpoint has fallen from around 81% to 76% since July 2024 as it appears more often at the start and end of journeys. Discovery is also moving to places traditional measurement doesn’t reach, such as marketplace-native assistants that apply their own ranking rules. Agentic checkout protocols are starting to let shoppers complete the whole purchase inside a single AI experience, which will widen the measurement gap further.

Leaders should revisit this assumption using:

●     Journey-level data: how often AI and Search co-occur, and how conversion differs between journey types.

●     AI visibility and citation data: where and how the brand appears across AI platforms. AI recommendations also carry beyond the session. In the report’s Finance, Travel and Beauty examples, users were consistently more likely to visit the AI-recommended brand than its competitor within seven days.

●     Marketplace-native growth: the growth of assistants inside marketplaces compared with external AI referrals.

●     Category data: AI’s role varies by category (14.1% of journeys in Consumer Electronics and 13.2% in Beauty, against 10.4% in Home & Garden). Growth is concentrated where innovation is becoming accessible, such as wearables, robot vacuums and K-Beauty, which win attention out of proportion to their shelf space by bringing premium technology to a mainstream price point.

The planning unit should shift from the channel to the whole journey, covering product data, paid media, on-platform AI and pricing together. Consumers do still want control over the final decision, but full agentic commerce is on the horizon. How quickly it will be adopted is still unclear, but it could change the game once again.