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AI is changing ecommerce from product discovery to fulfillment, but its benefits are possibilities—not guaranteed sales gains or cost savings. Predictive AI finds patterns in data to forecast demand or recommend products; generative AI creates or summarizes content and supports conversational shopping. The most useful applications combine both with reliable data, sound workflows, and human oversight.
How AI is already influencing online shopping
AI is becoming part of the customer journey before a shopper reaches a retailer’s site. In a global survey conducted in Q3 2025, 45% of more than 18,000 consumers across 23 countries said they had used AI for help during buying journeys. Respondents used it to research products (41%), interpret reviews (33%), and hunt for deals (31%). These figures come from the IBM Institute for Business Value and the National Retail Federation’s survey; they describe those respondents, not all online shoppers. IBM and NRF, January 7, 2026.
For retailers, the shift creates an incentive to make product information accurate and easy for both people and AI tools to interpret. It does not establish that AI use increases purchases or loyalty. As NRF’s Caroline Reppert put it, retailers must understand how AI is shaping discovery and comparison if they want to earn shoppers’ trust and remain relevant.
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Predictive AI and generative AI do different jobs
“AI” in ecommerce covers methods with distinct roles. Predictive and analytical systems learn from historical or live data to estimate what may happen or identify patterns. Generative AI produces new text, images, summaries, or conversational responses, often in response to a prompt. Many practical applications join the two: an analytical model supplies a forecast or recommendation, while a generative assistant explains it or helps an employee act on it.
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- Predictive and analytical AI: recommends products, segments customers, detects unusual transactions, estimates demand, or informs inventory and pricing decisions.
- Generative AI: drafts product descriptions and marketing copy, summarizes reviews or support history, and responds conversationally to shoppers or staff.
Neither type makes an unreliable catalog, incomplete customer history, or weak process reliable by itself. The quality of its inputs and the way its output is used matter.
Where ecommerce businesses can use AI
Product discovery and recommendations
Recommendation systems can use purchase history, browsing signals, and stated preferences to rank relevant products, bundles, or add-ons. A generative shopping assistant can make exploration more conversational: a shopper can describe a need, ask follow-up questions, and narrow options. These approaches may make discovery more relevant, but a recommendation is only as useful as its product data and fit to the shopper’s actual needs.
Customer service and shopping assistance
Conversational assistants can answer product questions, suggest options, explain policies, and help shoppers manage carts. They can also support service agents by surfacing order details or summarizing a conversation. McKinsey’s April 2024 survey of 52 global Fortune 500 retail executives found that 82% reported customer-service generative-AI pilots and 36% reported scaling in that area. The survey also found that 90% had begun experimenting with generative AI. These are dated results from a small executive sample, not current adoption rates for ecommerce businesses generally. McKinsey, Generative AI in retail: LLM to ROI.
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A bot that cannot access accurate order or product information can frustrate customers. Retailers need clear handoffs to a person for unusual, sensitive, or unresolved cases, and should measure service quality rather than treating the presence of a chatbot as success.
Marketing and product content
Generative AI can help teams draft or adapt product descriptions, campaign messages, and other customer communications. It may make it easier to tailor content by audience or channel, but the output needs review for accuracy, brand fit, accessibility, and legal compliance. AI-generated content should not silently invent product specifications, availability, or claims.
Merchandising, pricing, and assortment
Analytical tools can help teams examine sales patterns, customer segments, and product performance when deciding what to feature, stock, or price. Generative tools can help employees query large datasets or summarize possible explanations for a trend. These systems inform decisions; they do not remove the need to consider margin, supplier constraints, customer expectations, and the consequences of a pricing or assortment change.
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Forecasting, inventory, and fulfillment
Demand forecasts can support allocation, replenishment, and staffing decisions. AI may also help identify exceptions in order processing and fulfillment. If forecasts are built on incomplete or outdated sales and inventory data, they can mislead teams; if the recommendation is not connected to purchasing or warehouse workflows, it may never affect operations.
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Orders, payments, and security
AI applications can support order intelligence, payment-related processes, and security by helping classify activity or identify anomalies for review. These are potential use cases, not a guarantee that fraud will be prevented or that payment performance will improve. Decisions with customer or financial consequences require appropriate controls and escalation paths.
New commerce channels
AI may support marketplace, voice, social, and experiential commerce by helping shoppers find and evaluate products in different interfaces. These are areas of application rather than established results for every retailer; the right channel depends on where customers actually shop and whether product, inventory, and service information remain consistent across it. IBM, AI in commerce: Essential use cases for B2B and B2C.
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What the adoption figures do—and do not—show
Adoption is rising, but the available figures cover different populations and should not be read as a single ecommerce adoption rate. Eurostat reports that 20% of EU businesses used AI in 2025, up from 13% in 2024; reported use was 55% among large businesses and 19% among small and medium-sized enterprises. The measure covers businesses across the EU, not ecommerce companies alone. Eurostat, Digitalisation in Europe – 2026 edition.
Retail-specific executive surveys suggest experimentation is ahead of broad, embedded adoption. In a March 2026 survey of 36 retail executives, McKinsey and EuroCommerce found 40% described their AI strategy as developing, while fewer than 30% described it as established or embedded. More than 80% were at emerging or developing levels of AI literacy and adoption. The small-sample results indicate reported organizational maturity among surveyed executives, not a census of European retailers. McKinsey and EuroCommerce, Rewiring retail in Europe: The AI imperative.
AI is not yet evidence that ecommerce problems are solved
Online shopping still has basic reliability and service problems. In 2025, 35% of EU residents who had bought online during the previous three months reported a problem with a website or app. Slower delivery than indicated was reported by 20%, difficult or unsatisfactory website use by 11%, and wrong or damaged goods or services by 10%, according to Eurostat. These figures provide context about ecommerce friction; they do not show that AI caused or resolved it.
AI could help a retailer detect delivery exceptions, improve support responses, or make product information easier to navigate. Whether it does so depends on implementation, and an automated interface can add friction when it gives incorrect answers or blocks access to human help.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to adopt AI without creating more problems
- Start with a defined business problem. Choose a specific task—such as reducing unanswered product questions or improving forecast review—and set a measurable outcome. Compare results with an appropriate baseline; do not assume a model will improve sales, conversion, or costs.
- Check the data. Review catalog completeness, product attributes, inventory freshness, order histories, and any customer data the tool will use. Decide what data is necessary and how privacy and security requirements will be met.
- Select the right approach. A ready-made tool may suit a common task; a customized model or workflow may be warranted when the business has distinctive data or needs. Do not use generative AI where a simpler analytical rule or established system is more suitable.
- Connect output to real work. Determine who acts on a recommendation, where it appears, and how it reaches existing commerce, service, or operations systems. Involve employees who will use it and provide a way to correct errors.
- Set controls and monitor results. Define human review, escalation, security, and governance. Track accuracy, customer experience, workflow adoption, and unintended effects, then revise or stop the use case if it fails its goals.
Recurring obstacles include fragmented or poor-quality data, legacy systems, limited skills, privacy and security concerns, implementation costs, weak workflow integration, and insufficient governance. McKinsey and EuroCommerce describe transformation as requiring capabilities across strategy, data, technology, talent, workflow, and governance—not just a model purchase.
What comes next: AI-assisted and orchestrated commerce
The emerging direction is commerce in which AI tools help shoppers search, compare, choose, buy, and receive products across retailer sites and other platforms. McKinsey and EuroCommerce describe this as “orchestrated commerce,” a developing second wave in which assistants could connect discovery and purchase more directly. Retailers will need dependable product and availability data, connected systems, and clear accountability for recommendations and transactions.
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