AI adoption in e-commerce is already broad, but the market is still uneven. Many commerce organizations are evaluating or experimenting with AI, while fewer have fully embedded it across customer journeys, operations and decision-making.
This page is part of the AI Commerce Statistics silo and the broader E-commerce Statistics hub. It focuses on how online retailers, commerce teams and retail organizations are adopting AI across marketing, personalization, product discovery, customer service, content, forecasting and fraud prevention.
AI adoption in e-commerce: quick answer
The best way to read AI adoption in e-commerce is not as one universal percentage. The number changes depending on whether the source measures “considering AI,” “evaluating AI,” “experimenting with AI,” “fully implemented AI,” or “regularly using generative AI.”
Commerce organizations considering AI
Salesforce reported that 97% of commerce organizations are at least considering the use of artificial intelligence.
Experimenting with AI
In Salesforce’s commerce survey, nearly half of commerce organizations were experimenting with AI in e-commerce operations.
Fully implemented AI
Salesforce reported that 29% of commerce organizations had fully implemented AI in e-commerce operations.
Organizations regularly using generative AI
McKinsey’s broader global AI survey reported that 71% of organizations regularly use generative AI in at least one business function.
Key AI adoption statistics for e-commerce
These figures combine commerce-specific adoption data with broader AI adoption benchmarks that help explain the wider business context.
| Statistic | What it measures | Source context |
|---|---|---|
| 97% of commerce organizations are at least considering AI | Commerce AI interest or evaluation, not necessarily active implementation | Salesforce State of Commerce, commerce leaders and buyer data |
| 48% are experimenting with AI | Commerce organizations testing or piloting AI in e-commerce operations | Salesforce State of Commerce |
| 29% have fully implemented AI | Commerce organizations reporting AI as fully implemented in e-commerce operations | Salesforce State of Commerce |
| 20% are evaluating AI | Commerce organizations assessing AI before active experimentation or deployment | Salesforce State of Commerce |
| 3% have no intention to use AI | Commerce organizations with no plans to use AI | Salesforce State of Commerce |
| 78% of organizations use AI in at least one business function | Broad organizational AI adoption, not limited to e-commerce | McKinsey Global Survey on AI |
| 71% of organizations regularly use generative AI in at least one business function | Broad generative AI usage across organizations | McKinsey Global Survey on AI |
| 45% of retailers apply generative AI for next-best-action recommendations | Retail use of generative AI for customer decisioning and recommendation-related use cases | Adobe 2025 AI and Digital Trends Retail |
| 45% of retailers use generative AI to personalize experiences based on real-time behavior | Retail personalization use case adoption | Adobe 2025 AI and Digital Trends Retail |
AI adoption status in commerce organizations
The most useful commerce-specific split is not “using AI vs. not using AI.” It is the difference between evaluation, experimentation and full implementation.
| Status | Share | How to interpret it |
|---|---|---|
| Fully implemented AI | 29% | AI is already deployed as part of e-commerce operations, although depth of implementation may vary by company. |
| Experimenting with AI | 48% | AI is being tested, piloted or used in limited workflows, but may not yet be fully embedded. |
| Evaluating AI | 20% | The organization is assessing AI opportunities, risks, vendors or use cases before deeper adoption. |
| No intention to use AI | 3% | Only a very small share of commerce organizations reported no plans to use AI. |
Where e-commerce teams are adopting AI
AI adoption in e-commerce usually starts in practical workflows where teams already have data, repetitive work, customer questions or measurable business outcomes.
Campaigns, targeting and content
E-commerce teams use AI to support campaign creation, audience segmentation, product copy, creative testing and marketing analysis.
Recommendations and product discovery
AI can support product recommendations, next-best-action suggestions, onsite search, bundles, cross-sells and guided selling.
Personalization and service automation
AI can personalize product journeys, answer customer questions, summarize support issues and automate order-related responses.
Forecasting, inventory and risk
AI can help with demand forecasting, inventory decisions, pricing signals, fraud detection, payment risk and return-related workflows.
AI adoption by segment and company type
AI adoption is not equal across every e-commerce business. Larger organizations, mature retail teams and data-rich businesses usually have an easier path to implementation than small stores with fragmented systems.
| Segment | Likely adoption pattern | Why it matters |
|---|---|---|
| Large retailers and marketplaces | More likely to adopt AI across recommendations, personalization, support, forecasting and fraud prevention | They usually have more data, larger teams, clearer ROI cases and more technical infrastructure. |
| Mid-market e-commerce brands | Often start with marketing, product content, personalization tools and customer service automation | They can adopt AI through SaaS platforms without building large internal AI teams. |
| Small online stores | Often use AI through embedded tools inside platforms, apps, email tools, ad platforms or chat widgets | Adoption may be real, but the store may not describe it as a formal AI strategy. |
| B2B commerce | AI can support search, product matching, quoting, account-specific recommendations and customer support | B2B adoption may focus less on flashy shopping assistants and more on operational efficiency. |
| Retail and consumer products organizations | Generative AI use cases often appear in marketing, e-commerce, customer service, product content and research workflows | Retail AI adoption may include both online and offline commerce, so the scope should be checked before citing data. |
Regional and market interpretation
AI adoption data is often global, North America-heavy, Europe-heavy or based on a vendor’s customer base. Before comparing markets, check the geography, respondent role and definition of “adoption.”
Global AI adoption data
Global business AI surveys are useful for understanding the overall direction of AI adoption, but they are not always e-commerce-specific. Use them as context, not as a replacement for commerce-specific adoption benchmarks.
Retail AI adoption data
Retail data may include physical stores, omnichannel retailers, consumer products companies and online commerce teams. This makes it useful, but not always identical to pure e-commerce data.
Vendor benchmark data
Vendor data can show real usage inside a platform, but it may overrepresent companies that are already more technologically mature or already interested in AI tools.
Methodology notes
AI adoption statistics can be misleading when different definitions are combined without context. Use the notes below before citing a single adoption number.
| Issue | Why it matters | How to handle it |
|---|---|---|
| Considering AI vs. using AI | Interest in AI is much higher than mature implementation. | Separate “considering,” “evaluating,” “experimenting” and “implemented” in your analysis. |
| Commerce vs. retail vs. general business | Retail and business AI surveys may include non-e-commerce use cases. | Use commerce-specific data first, then broader AI data as supporting context. |
| Generative AI vs. analytical AI | Some sources measure generative AI, while others measure all AI types. | Do not compare the numbers directly unless the source definitions match. |
| Vendor benchmarks | Vendor data may reflect a more tech-forward customer base. | Label vendor benchmarks clearly and avoid presenting them as the entire market. |
| Fast-changing market | AI adoption is changing quickly, especially in marketing, service and content workflows. | Prefer recent sources and check publication date before quoting numbers. |
Cite this page
BestForEcommerce. “AI Adoption in E-commerce.” BestForEcommerce.com, 2026. Available at: https://bestforecommerce.com/ecommerce-statistics/ai-commerce/ai-adoption-in-ecommerce/