AI is influencing ecommerce before a shopper reaches checkout and behind the scenes in areas such as service, marketing, forecasting, and fulfillment. Its potential benefits—more relevant discovery, quicker assistance, and better-informed operations—depend on reliable data and sound implementation; adoption statistics do not prove that AI itself increases sales or lowers costs.
What AI in ecommerce means
“AI” covers different techniques, not one kind of system. Predictive and analytical AI uses data to estimate what may happen or identify patterns—for example, which products a shopper may prefer or how much stock a retailer may need. Generative AI creates or transforms content, such as conversational answers, product descriptions, or marketing messages. A retailer may combine them: analytics can produce a recommendation, while a generative interface explains it or helps a shopper refine it.
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These capabilities can support decisions and automate parts of a workflow. They do not guarantee a better customer experience or a business result; the outcome depends on data, integration, oversight, and the problem being addressed.
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How widely are consumers and businesses using AI?
A global consumer survey conducted in Q3 2025 by the IBM Institute for Business Value and the National Retail Federation included more than 18,000 respondents across 23 countries. In that survey, 45% said they had turned to AI for help during buying journeys. Respondents reported using it to research products (41%), interpret reviews (33%), and hunt for deals (31%). These figures describe that survey’s respondents, not all shoppers or ecommerce purchases. IBM and NRF, January 7, 2026.
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For businesses, Eurostat reports that 20% of EU businesses used AI in 2025, compared with 13% in 2024. The 2025 figure was 55% among large businesses and 19% among SMEs. This is a measure across businesses in the EU, not an ecommerce-only adoption rate. Eurostat, Digitalisation in Europe – 2026 edition.
Retail-specific surveys offer a different, narrower snapshot. In an April 2024 survey of 52 global Fortune 500 retail executives, 90% said their organizations had begun experimenting with generative AI; 82% reported customer-service pilots and 36% said they were scaling in customer service. These dated, small-sample findings should not be read as current adoption rates for the retail industry as a whole. McKinsey, Generative AI in retail: LLM to ROI.
Where ecommerce teams can use AI
Product discovery and recommendations
Predictive models can use signals such as purchase history and stated preferences to rank products, suggest complementary items, or tailor recommendations. Generative AI can make discovery more conversational: a shopper might describe a need, ask follow-up questions, and narrow options in ordinary language. These tools may make product exploration more relevant, but a recommendation is only as useful as its catalog information and the signals behind it.
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Shopping assistance and customer service
Conversational assistants can answer product questions, guide shoppers through options, and help with tasks such as managing a cart or finding order information. Retailers are experimenting with assistants earlier in the purchase journey, not only in post-purchase support. Personalization based on preferences and order history can make an assistant more useful, provided the information is accurate and its limits are clear. McKinsey’s 2024 retail analysis discusses these experiments.
For human service teams, AI may also summarize interactions or help locate relevant information. Automation should not become a dead end: customers need a workable route to a person when a question is unusual, sensitive, or unresolved.
Marketing and product content
Generative systems can draft or adapt product copy, campaign material, and customer communications. They may help teams produce variations for different audiences or channels, but generated text still needs review for factual accuracy, brand voice, rights, and policy compliance. McKinsey and EuroCommerce’s 2026 European retail report describes generative AI as changing marketing and content creation, with potential for more personalized, engaging, and timely communication. McKinsey and EuroCommerce, June 10, 2026.
Forecasting, inventory, and merchandising
Analytical AI can help estimate demand, inform inventory allocation, and support replenishment decisions. Models may also help teams examine pricing, customer segments, and assortment choices across large volumes of sales and product data. Generative AI paired with analytics can make it easier to ask questions about a forecast or explore possible drivers; it does not replace the underlying data or the judgment needed to act on it.
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AI can support operational workflows such as order processing and fulfillment, and can be applied to order intelligence and payment or security tasks. Potential uses include identifying patterns for review or helping teams prioritize exceptions. These applications need controls appropriate to the financial, privacy, and service risks involved. IBM’s overview of AI in commerce describes these areas alongside shopper-facing applications.
New channels and commerce models
AI may support marketplace, voice, social, or experiential commerce by helping customers search, compare, and act in different interfaces. These are potential application areas, not evidence that every retailer can profitably adopt every channel.
What benefits are realistic—and what is not established?
When deployed well, AI may reduce the effort involved in finding relevant products, provide faster access to useful information, help teams work through large data sets, and automate repeatable tasks. Those are capabilities, not guaranteed outcomes. The sources cited here do not establish an independently measured, ecommerce-specific causal effect of AI on sales, conversion, or operating costs. A high adoption figure or a successful pilot does not prove that AI caused a business improvement.
AI also does not automatically fix basic ecommerce service problems. Eurostat reports that 35% of EU internet users who bought online in 2025 encountered a problem with a website or app; 20% reported delivery slower than indicated, 11% difficult or unsatisfactory website use, and 10% wrong or damaged goods or services. These figures describe ecommerce friction, not problems that AI has been shown to solve. Eurostat, Digitalisation in Europe – 2026 edition.
What makes an AI project work—or fail?
Retail AI depends on more than the model. Fragmented or inaccurate product and customer data can produce irrelevant recommendations or incorrect answers. Legacy systems may make it hard to connect a tool to inventory, order, or service workflows. Teams also need appropriate skills, privacy and security safeguards, governance, and a clear way to check quality. IBM warns that inadequate or inappropriate data can create poor experiences that alienate consumers and businesses; it identifies trust in data, security, brand, and people as central to successful integration. IBM, April 23, 2025.
McKinsey’s 2024 retail research identifies data quality, privacy concerns, limited expertise or resources, and implementation expense as obstacles to scaling. Its 2026 report adds fragmented data, legacy technology, capability gaps, and weak workflow adoption. The report groups transformation capabilities into strategy, data, technology, talent, workflow, and governance. McKinsey and EuroCommerce, June 10, 2026.
A practical way to evaluate a use case
- Start with a specific problem. Define the customer or operational issue and the outcome that would count as improvement. Avoid treating “add a chatbot” as a strategy.
- Check the inputs and safeguards. Review catalog accuracy, customer-data quality, privacy, security, and who is accountable for errors.
- Choose the right approach. Decide whether a ready-made tool is sufficient or whether the use case requires an adapted model or integration with existing systems.
- Fit the work into real workflows. Connect AI outputs to the systems employees use, establish human review where needed, and give customers a path to support.
- Monitor outcomes and trust. Assess service quality and the chosen business measure over time, including errors and cases where the system should defer rather than answer.
What comes next: AI-assisted and orchestrated commerce
The next direction is commerce in which AI tools on retailer sites and other platforms help shoppers search, choose, buy, and receive products. McKinsey and EuroCommerce describe this as “orchestrated commerce”: a developing second wave in which AI may coordinate more of the journey across discovery and fulfillment. It is a direction retailers are preparing for, not a settled description of how all shopping already works.
Readiness remains uneven. In a March 2026 survey of 36 retail executives, 40% reported having a developing AI strategy, while fewer than 30% said their strategy was established or embedded; more than 80% were at emerging or developing levels of AI literacy and adoption. The small executive sample is a snapshot, not a forecast of the entire sector. The report says scaled results remain uneven and emphasizes modernization of underlying capabilities. McKinsey and EuroCommerce, June 10, 2026.
For shoppers, the near-term change is that AI is becoming another way to research, compare, and get assistance. For retailers, the opportunity is to make those interactions useful while improving the systems behind them. Whether that becomes a durable advantage will depend less on the novelty of a model than on trustworthy information, well-designed workflows, and a measurable customer or operational need.
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