AI in E-commerce Statistics 2026

AI moved from the back office to the storefront in 2024, and the numbers are now large enough to take seriously. Nearly one in five holiday purchases was AI-influenced, generative-AI shopping traffic is compounding fast, and retailers that deployed agents saw measurable lifts. The figures below come from Salesforce's holiday data, Adobe Analytics, and McKinsey's State of AI survey — each linked so you can verify before you cite.

AI now shapes one in five purchases

The clearest signal that AI has reached retail's core comes from Salesforce's 2024 holiday data: 19% of holiday purchases were influenced by consumers engaging with AI and agents — six points higher than the year before — representing roughly $229 billion of global online orders. When nearly a fifth of holiday spending touches an AI interaction somewhere in the journey, AI is no longer an experiment at the edges of commerce; it is woven into how people discover, compare, and decide.

That same season, Salesforce reported U.S. online sales of about $282 billion across November and December, with AI chatbot use up roughly 42% year over year. The combination matters: not only is overall spending growing, but the share of it shaped by AI is growing faster, which means AI's influence on retail compounds rather than plateaus. For how this connects to selling motions, see our AI in sales statistics and the wider AI statistics hub.

Generative AI is a new front door to stores

A second shift is where shoppers start their journey. Adobe Analytics found that traffic to U.S. retail sites from generative-AI sources surged enormously year over year during the 2024 holiday season, with that growth continuing into 2025. The base is still small next to paid search and email, but the slope is steep — shoppers increasingly ask an AI for product research, deals, and gift ideas before they ever land on a store page.

This reframes a familiar problem. For two decades, retailers optimized for search engines and social feeds as the front door; now a third front door is opening, one where an AI assistant mediates between shopper and store. The retailers that show up well in those AI-mediated answers — with clean, structured, trustworthy product information — stand to capture demand that competitors never see. The ones that ignore it risk being summarized out of the conversation.

Fast growth off a small base: Generative-AI referral traffic is growing at eye-catching percentages, but it remains a minor share of total retail traffic today. Treat it as an emerging channel to prepare for, not yet the dominant one — and verify the latest figures against Adobe's reports.

How widely retailers have adopted AI

Retail adoption tracks the broader corporate picture. McKinsey's State of AI survey finds about two-thirds of organizations use AI in at least one business function, and in commerce that energy concentrates in a handful of high-value areas where the payoff is quickest to measure. Crucially, adoption is broad but depth is uneven — many retailers use AI somewhere, far fewer have scaled it across the enterprise into a coordinated capability.

This breadth-versus-depth gap is the defining feature of retail AI right now. It is easy to bolt a chatbot onto a storefront or switch on a recommendation widget; it is much harder to connect personalization, search, service, and inventory into a system that learns from one another. The retailers pulling ahead are the ones closing that gap, not the ones with the longest list of pilots.

AI in e-commerce — headline indicators

Indicator	Value	Source
Holiday purchases AI-influenced (2024)	19%	Salesforce
Global online orders influenced by AI	~$229B	Salesforce
AI chatbot use vs prior year	+42%	Salesforce
U.S. online holiday sales (Nov–Dec 2024)	~$282B	Salesforce
Organizations using AI in ≥1 function	~2 in 3	McKinsey

Where AI earns its keep in commerce

The use cases that drive the numbers above cluster in predictable places — the parts of the shopping journey where personalization and speed matter most. They split naturally into customer-facing experiences and behind-the-scenes operations, and the strongest retailers invest in both.

• Personalized recommendations and merchandising tuned to each shopper.
• Conversational shopping assistants and chatbots — see our AI marketing statistics.
• Search and discovery that understands natural-language queries.
• Customer service and post-purchase support.
• Demand forecasting, pricing, and inventory optimization behind the scenes.

Customer-facing: discovery and service

On the storefront, AI's job is to shorten the path from intent to purchase. Personalized recommendations surface the right product, natural-language search lets shoppers describe what they want in their own words, and conversational assistants answer questions that would otherwise cause abandonment. These are the experiences that show up in the Salesforce influence figures, because they touch the shopper directly.

Behind the scenes: forecasting and ops

Out of sight, AI tunes the machinery of retail: forecasting demand so stock matches it, adjusting pricing dynamically, and optimizing inventory and fulfilment. These uses rarely make headlines, but they often deliver the cleanest return, because they attack measurable costs like overstock, stockouts, and markdowns rather than the fuzzier goal of a better experience.

From assisted to agentic shopping

The frontier is "agentic commerce" — shopping where AI agents help discover, compare, and even complete purchases on a shopper's behalf, rather than merely answering questions. Salesforce's data already shows AI-influenced orders growing fast, and analysts project that agents could orchestrate large shares of retail revenue over the coming years. These longer-range projections are scenarios, not certainties, but the direction is consistent with what the holiday data already shows on the ground.

If agents become the buyers' proxies, the rules of merchandising change. An agent comparing options cares about structured, machine-readable facts — price, availability, specs, reviews — more than about banner creative or homepage layout. Preparing for that world is less about a flashy launch and more about getting the fundamentals of product data and APIs right now, so your catalog is legible to the agents that will increasingly mediate demand.

1. Make your catalog and content readable by AI assistants, not just humans.
2. Deploy and measure shopper-facing agents on real journeys before scaling.
3. Treat generative-AI referral traffic as a channel to instrument now.
4. Watch for agent-driven checkout standards as they emerge.

Reading retail AI data without getting fooled

The headline figures are encouraging, but they reward careful reading. "Influenced by AI" is a broad phrase: it can mean a shopper used an AI assistant somewhere in the journey, not that AI closed the sale on its own. That makes the 19% figure a measure of reach and touch, not of direct, attributable conversion — useful, but easy to overstate if you treat it as pure attribution.

Similarly, the eye-popping growth in generative-AI referral traffic is real but starts from a tiny base, so a four-figure percentage increase can still be a small share of total visits. And lift figures from a single holiday season are a snapshot, not a guarantee. The honest way to use this data is to track the direction and your own numbers over time, rather than lifting a headline percentage out of context.

Touch is not the same as attribution: An AI-influenced purchase means AI was somewhere in the journey, not that it solely caused the sale. Keep that distinction when you report internal numbers, and measure incremental lift where you can rather than assuming the touch caused the conversion.

What this means for 2026

Retail has crossed a threshold: AI now influences a measurable, multi-hundred-billion-dollar slice of online spending, generative AI is a real if early front door, and the brands that deployed agents saw the strongest growth. The strategic gap in 2026 is less about adopting AI at all — most retailers already have — and more about scaling it past scattered pilots into the core shopping journey, and doing so without fooling yourself about what the metrics mean.

If you run a store, start where AI most directly improves discovery and service, instrument generative-AI traffic so you can watch it grow, and prepare your catalog and product data for an agentic future where machines, not just people, read your listings. Our AI customer research guide and the rest of our AI statistics help you act on these trends with eyes open.

Sources & references

Every figure in this article links to its primary source below. Follow the links to confirm exact definitions, scope, and methodology before citing.

• Salesforce — 2024 Holiday Shopping Data (2025)
• Adobe — Generative AI-Powered Shopping (Adobe Analytics) (2025)
• McKinsey — The State of AI (Global Survey) (2024)