A Benchmark Built for a Different Shopper
On July 15, Digital Commerce 360 and ReFiBuy launched the AI Commerce Rankings, a quarterly benchmark that scores the Top 1000 North American retailers on how prepared they are for AI-driven shopping and agentic product discovery. It extends a 25-year-old database that has always ranked retailers by online sales, and it adds a forward-looking measure of readiness for a buyer who increasingly is not a human clicking through a homepage. As Brian Warmoth, Editor-in-Chief at Digital Commerce 360, put it, as AI-driven shopping becomes meaningful in product discovery, retailers need new ways to understand their positioning. The scoreboard retail has used for two decades is being supplemented by one built for machine buyers.
The methodology, co-developed by ReFiBuy, combines four signals into a single readiness score. Bot friendliness measures whether AI agents and agentic platforms can access and read a retailer's catalog data. AI source traffic captures the share of a site's visits coming from AI-powered discovery. Diversity of AI sources checks whether that traffic spans multiple engines rather than depending on one. And 90-day momentum tracks whether AI-sourced traffic is rising or falling. Jon Love, Research Data Manager, said the rankings add essential insight to evaluate readiness for consumer behavior shifts. The composite gives executives a number they can benchmark and defend.
The Scores Expose a Wide Gap
The headline result is sobering for the industry. The average readiness score across all 1000 retailers is 41.9 out of 100, and only 20 retailers scored above 60. That distribution tells enterprise leaders that AI shopping readiness is not yet a solved problem even among the largest online sellers, and that the field is wide open for retailers willing to invest ahead of the curve. A benchmark where four out of five participants sit below the midpoint is describing an early market, one where the gap between intention and capability remains large and where a deliberate program can move a retailer up the ranking quickly.
The composition of the leaderboard is the more revealing detail. Online Labels, Nixon, Fashionphile, Everlane, and Brooklinen lead the rankings, and none of them ranks near the top of the Top 1000 by online sales. In other words, readiness for AI shopping does not correlate cleanly with size. Focused mid-sized specialists with clean catalogs, coherent product data, and modern storefronts are outscoring far larger incumbents whose sprawling estates and legacy platforms make them harder for an agent to parse. For a retail CTO, that decoupling of scale from readiness is both a warning and an opening.
Why Bot Friendliness Became a Commercial Metric
For most of ecommerce history, blocking or ignoring bots was sound practice, since automated traffic meant scraping, fraud, or scalping. That instinct is now a liability. When a growing share of shoppers delegate discovery to an AI assistant, the agent visiting a retailer's site on a customer's behalf is the customer, and a catalog it cannot read is a store it cannot recommend. Bot friendliness measures exactly this: whether structured product data, pricing, and availability are exposed in ways an agent can parse and trust. Retailers who reflexively wall off automated traffic risk becoming invisible to the very intermediaries that now sit between them and demand.
The engineering implications run deeper than a robots.txt edit. Being legible to agents means clean, structured, and consistently updated product data, reliable feeds, and machine-readable inventory and pricing. These are the same data foundations that retailers have underinvested in for years while pouring budget into front-end experience. The rise of agentic discovery raises the cost of that neglect, because a human shopper will forgive a messy catalog and search for what they want, while an agent simply moves on to a competitor whose data it can consume. Product data quality has become a demand-generation asset.
The Traffic Shift Is Already Underway
This is not a speculative future. Adobe Analytics data cited alongside the launch shows AI-source traffic to United States retail sites up 393 percent year over year in the first quarter of 2026, after surging 693 percent during the 2025 holiday season and remaining elevated into the new year. Traffic arriving through AI assistants has also tended to convert at higher rates than conventional search, because a shopper who reaches a product through an agent has often already had their needs filtered and matched. The channel is small relative to search today, and it is growing at a rate that makes it impossible to dismiss as a novelty.
For retail leaders, that growth curve reframes the readiness score from a vanity metric into a revenue question. Every point of AI-source traffic a retailer fails to capture is demand routed to a competitor whose catalog the agents can read. The compounding nature of these shifts means the retailers who optimize early will accumulate an advantage in AI-driven discovery that laggards will struggle to reverse once shopping habits settle. Waiting for the channel to mature before investing is a decision to cede position during the exact window when position is cheapest to build.
What Retail Technology Leaders Should Do Now
The practical agenda starts with an honest audit. Retail CTOs should assess how legible their catalog is to AI agents, measure the AI-source traffic they already receive, and identify where structured data and feeds break down. The four signals in the ranking double as a diagnostic checklist, and the fact that mid-sized specialists lead the field shows that focused effort beats raw budget. This is a domain where a disciplined data and platform program can produce visible movement in a quarter, precisely because so many competitors have not started. The benchmark gives executives a defensible way to size the gap and track progress against it.
The strategic point for CIOs and CTOs is that AI shopping readiness is a technology and data problem before it is a marketing one. Getting it right means treating product data as core infrastructure, exposing it to trusted agents deliberately, and measuring the channel with the same rigor applied to paid search and organic traffic. The AI Commerce Rankings will update quarterly, which means the scoreboard will keep moving and standing still will show up as a falling score. The retailers who internalize that dynamic will be the ones the next generation of shopping agents actually recommends.
