Benchmarks / All stores / v2.0.6

Agentic commerce benchmark across all stores v2.0.6

An AI shopping agent reached the payment step on 88% of the 242 stores Ottom measured at every stage in the 90 days to 10 October 2026, in every category and including stores whose category could not be determined, and the median agentic readiness score was 97 out of 100. Cite

This benchmark pools every store Ottom has scanned, across all categories, including stores whose category could not be determined. Each store is counted once, whichever category it is in. It is counted under exactly the rules each category is, set out in the methodology.

n = 242 measured of 255 scanned · 12 July 2026 to 10 October 2026 · v2.0.6 · bench-v5 · methodology

Scores come from the 242 stores Ottom measured at every stage. A further 13 were scanned but stopped before checkout, usually at bot protection or a robots.txt rule, so they have no comparable score. All 255 are still counted in the stalls below, because what was found on a store does not depend on how it was scored.

A store enters Ottom’s survey only if a catalogue of at least ten products can be read from it in one request, which in practice means Shopify and WooCommerce storefronts. These figures therefore describe the storefronts Ottom can read in bulk rather than online retail as a whole. The methodology sets out what that excludes.

Between 1 and 10 October 2026 the median score across all stores rose from 55 to 97. The stores did not change. Our agent did. On 4 October 2026 we replaced the scripted agent that walked checkout, agent-v1, with a new one, agent-v2, and from 8 October 2026 we measured every surveyed store again with it. On 250 stores measured by both, the old agent reached the payment step on 5 and the new one on 212. Figures before 10 October 2026 were measured wholly or partly by the old agent and are not compared with figures after it. The methodology sets out which agent measured these figures.

Reaching the payment step

88% of the 242 stores Ottom measured let an AI shopping agent reach the payment step. Ottom stops there and never submits payment. Cite

Where agents stall

Of the 242 stores Ottom measured at every stage, 81% scored 80 or better at the discover stage, against 91% at the compare stage. Cite

Each stage of the journey, and the share of stores whose score at that stage was 80 or better.

What stops them

The most common stall across all stores is products have no GTIN or MPN to match against, found on 63% of the 255 stores Ottom scanned in the 90 days to 10 October 2026. Cite

The most common stalls across all stores, as a share of all 255 stores scanned. A stall found on fewer than 5 stores is withheld, because at that count it points at a merchant rather than at a pattern.

What to change first

• No product identifier

  Populate the barcode field (GTIN, EAN or UPC) on each variant, which most platforms already have a field for.

• Variants undescribed

  Add hasVariant entries to the Product markup, one per purchasable combination, each with its own offer carrying price and availability.

• No sitemap

  Publish /sitemap.xml covering product, collection and policy URLs. Most platforms generate this for you once it is enabled.

7 further stalls were found but withheld: too few stores showed them to report a share without pointing at an individual store.

Readiness score

The median of the 242 stores Ottom measured at every stage in the 90 days to 10 October 2026 scored 97 out of 100, and a quarter of them scored 93 or below. Cite

The spread of overall readiness scores across the category. We publish quartiles rather than a best and worst store, because the best and the worst are each one identifiable merchant.

Most stores score near the top: three quarters score 93 or more, and the middle store scores 97. Reaching the payment step is worth 40 of the 100 points, and a store where the agent did not reach it can earn at most 18 of those 40. So the score separates the stores an agent can buy from from the stores it cannot, and does not separate stores within each group by much.

Source: Ottom, <a href="https://ottom.io/benchmarks/all">Agentic commerce benchmark across all stores v2.0.6</a>, 12 July 2026 to 10 October 2026, methodology bench-v5. Licensed under <a href="https://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0</a>.