Generative AI, intelligent product search, automated customer service and data-driven processes are reshaping retail. How can retailers and brands put AI to practical use? Which applications already deliver value – and what matters when getting started?
How is AI changing retail?
Artificial intelligence can analyse large volumes of data, create content and make information available when it is needed. In retail, this opens up new opportunities – from product discovery and customer service to marketing, assortment planning and internal operations.
Generative AI is also changing how consumers search for information and products. Tools such as ChatGPT, Gemini and Claude can answer questions, compare products and make recommendations. At the same time, AI assistants are becoming capable of handling individual steps in the shopping journey. For retailers and brands, this creates new digital touchpoints with potential customers.
The key is not to deploy AI as widely as possible, but to identify applications that offer real value to the business.
What are the benefits of AI for retailers?
AI can help businesses improve their operations, engage customers more effectively and make better-informed decisions.
- Automating routine tasks: AI can assist with recurring tasks such as placing orders, optimising prices or processing returns. This frees up employees to focus on advice and customer service.
- Better-informed decisions: Data-driven analysis can help businesses assess market trends, seasonal patterns and buying preferences, and respond more quickly to change.
- More personalised shopping experiences: Tailored product recommendations, intelligent search functions and AI-assisted advice enable retailers to respond more closely to customer needs.
- More efficient stock planning: AI-based forecasts can help reduce excess stock and shortages and make more effective use of resources.
Where is AI being used in retail?
AI is being used across many areas of retail – from personalised customer experiences to more efficient internal processes.
- Personalising shopping experiences: AI can make shopping more relevant to individual customers. Personalised product recommendations can take previous purchases or stated preferences into account. Visual search helps shoppers find products using images, while digital assistants can support product selection and advice.
- Improving AI search and product visibility: Instead of entering individual search terms, consumers can describe what they are looking for to an AI system. Retailers and manufacturers therefore need clear and complete product information – from features and use cases to prices and availability. Brands should also keep this information consistent across product feeds and retail partners. This can improve the conditions for discoverability, but does not guarantee inclusion in AI-generated answers.
- Enabling new shopping journeys through agentic commerce: AI assistants can research products, compare offers and, depending on the application and the permissions granted, support or complete individual steps towards a purchase. This makes product data, technical interfaces and the digital availability of offers increasingly important for retailers and brands. Not all these functions are available in every market yet.
- Improving customer service and knowledge management: Generative AI can make information from product databases, service materials and internal documents easier to access. Answer engines help employees find answers to customer questions more quickly and use them when advising shoppers. This requires information that is up to date, reliable and appropriately access-controlled.
- Supporting content and marketing: Generative AI can support content production, from product descriptions and translations to visual concepts. AI image tools can, for example, create variations of existing product imagery for different settings and communication channels. Editorial review remains essential: generated images and copy must not misrepresent the product.
- Data analysis and assortment planning: Using predictive analytics (data-driven forecasting) and machine learning, AI can analyse historical sales data, seasonal patterns and external factors such as the weather. This supports demand forecasting, stock management and assortment decisions. Reliable data is essential.
Digital strategist Björn Ognibeni explains how data-driven strategies are changing business models and retail operations, using examples from Chinese e-commerce.
How can businesses get started with AI?
Getting started does not necessarily require a large-scale technology project. A better approach is to begin with specific use cases and assess the benefits systematically.
- Define a use case
What task do you want to improve? Retailers could, for example, test whether AI saves time when writing product descriptions or helps staff answer customer questions more quickly. The aim should be clear and measurable.
- Review your data
Is your product and business information accurate, complete and accessible to the application? Reliable data is an important foundation for many AI use cases.
- Test applications in a targeted way
Small pilot projects can show which tools meet your needs. Data protection, information security and the risk of errors in AI-generated outputs should be considered from the outset.
- Evaluate results and build on what works
Does AI actually save time, improve the quality of customer advice or produce more reliable forecasts? Suitable performance measures help businesses assess results and expand successful applications.
What matters is the real value for customers and businesses – not the technology for its own sake. Futurist Ben Hammersley explains why this shift in perspective matters in his interview.
Frequently asked questions about AI in retail
What does AI in retail mean?
AI in retail means using artificial intelligence across business activities and processes, including product search, customer service, marketing, data analysis, stock management and assortment planning.
What is the difference between AI and generative AI?
Artificial intelligence covers technologies that can analyse data, recognise patterns or produce forecasts, for example. Generative AI is a subset of AI that can create new content, such as text, images or answers to questions.
How can products become visible in AI search?
Complete, structured product information can help digital systems understand and categorise products. Details about features, use cases, prices and availability may also be relevant. However, this does not guarantee that a product will appear in AI-generated recommendations.
What does agentic commerce mean?
Agentic commerce refers to shopping journeys in which AI assistants go beyond providing information to support or carry out tasks such as researching products, comparing offers or completing individual steps of a purchase.
How can retailers check their products’ visibility in AI search?
Retailers can test typical questions about their product category in ChatGPT, Gemini or other AI systems. Which brands, products and sources are mentioned? Repeated tests using different wording can help reveal information gaps. Results vary by system, timing and query.
