Agentic commerce
What are the latest agentic commerce statistics?
Updated
As of Q1 2026, AI-sourced traffic to US retail sites grew 393% year over year, and AI-referred visitors converted 42% better than non-AI traffic in March 2026 — a reversal from March 2025, when they converted 38% worse. Revenue per visit from AI referrals ran 37% above non-AI traffic. Retail product pages score 66% on machine readability, the lowest of any page type. McKinsey projects agentic commerce will mediate $900 billion to $1 trillion in US retail revenue by 2030 and $3–5 trillion globally.
Traffic
Adobe classifies AI-sourced sessions using referrer headers, user-agent strings and its own classification logic, covering ChatGPT, Claude, Copilot, Perplexity and Google AI Mode. The growth rates below are year-over-year for the stated window.
| Window | Growth vs. prior year |
|---|---|
| Nov–Dec 2025 (holiday) | +693% |
| Q1 2026 (Jan–Mar) | +393% |
| March 2026 alone | +269% |
Conversion and value
The reversal in quality is the most consequential trend in the dataset, because it is what turns agent traffic from a curiosity into a channel with a budget attached. A year separates the two rows below.
- By May 2026, AI-referred retail visitors converted 54% higher than non-AI traffic.
- Visitors arriving from AI assistants spend 48% more time on page.
- They view 13% more pages per visit and show a 12% higher engagement rate.
| Metric | March 2025 | March 2026 |
|---|---|---|
| Conversion rate vs. non-AI | 38% worse | 42% better |
| Revenue per visit vs. non-AI | 128% lower | 37% higher |
Merchant readiness
The readiness data is the merchant-side counterweight to the demand data, and it is where the opportunity actually sits.
- Product pages score lowest of any page type, and they are the pages an agent needs most — roughly a third of their content cannot be reliably parsed.
- The spread between retailers is wider than the spread between page types: 82.5% for the best-performing homepages against 54.2% for the lowest.
- The ranking is close to inverted. The pages retailers optimise least — returns, contact, FAQ — read best, because they are mostly text; the pages carrying the money are the most heavily built and the least legible.
| Page type | Readable share |
|---|---|
| Returns and exchanges | 82% |
| Contact us | 81% |
| FAQ | 80% |
| Customer service / help centre | 79% |
| Loyalty / membership | 78% |
| Homepage | 75% |
| Category page | 74% |
| Store locator | 73% |
| Product page | 66% |
Market forecasts
Forecasts deserve more scepticism than measurements, and the spread between the US and global figures below reflects genuinely different modelling assumptions rather than a single confident number.
- McKinsey moderate case: AI agents mediate $900 billion to $1 trillion in annual US Agentic Retail Revenue by 2030 — approximately 30% of projected B2C revenue across all retail categories.
- McKinsey global estimate: $3 trillion to $5 trillion in agentic commerce by 2030.
- ICSC survey, April 2026: 43% of consumers say they already trust agents for simple purchases.
How to read these numbers without over-reading them
Two cautions. First, the growth rates are against a very small base — 393% of a small number is still a modest share of total sessions for most retailers, and anyone quoting the percentage without the base is selling something.
Second, the conversion premium is partly a selection effect. An assistant that surfaces your product to a shopper who described what they wanted in a sentence has already done qualification that a broad-match paid click has not. That makes the traffic genuinely more valuable, but it does not mean the same visitor would have converted worse arriving another way.
The finding that survives both cautions is the readiness gap. Machine readability at 66% is a fact about your own site that you can measure today and fix regardless of how the forecasts land.
Frequently asked questions
- How does Adobe identify AI-sourced traffic?
- Through referrer headers, user-agent strings and its own classification logic, covering sessions arriving from ChatGPT, Claude, Copilot, Perplexity and Google AI Mode. It measures sessions that arrive at retail sites from those assistants rather than transactions completed inside them.
- Why did AI traffic go from converting 38% worse to 42% better in one year?
- Two effects compounding. Assistants got substantially better at surfacing a specific product that matches a stated need rather than a general page, and consumer behaviour shifted from experimenting with the tools to actually shopping with them. The result is traffic that arrives later in the funnel with the qualification already done.
- Is the US $1 trillion figure the same as the global $3–5 trillion one?
- No, they are different scopes from the same source. The $900 billion to $1 trillion figure is McKinsey's moderate case for annual US Agentic Retail Revenue by 2030, around 30% of projected US B2C retail. The $3–5 trillion figure is the global estimate for the same horizon.
Sources
- AI traffic grows but retail sites lag in AI search visibility (Q1 2026) — Adobe Digital Insights
- McKinsey forecasts up to $5 trillion in agentic commerce sales by 2030 — Digital Commerce 360, reporting McKinsey