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Generative AI in retail: How answer engines can strengthen customer service
24 Jun 2026
How can retailers make effective use of generative AI? Retail expert Miya Knights explains why customer service, knowledge management and AI-powered answer engines offer significant opportunities for the retail sector.
Generative AI is already creating tangible value in retail.
The greatest potential lies in customer service and employee support.
Answer engines provide fast access to product, service and business information.
Employees can support customers more effectively and efficiently.
Analysing employee queries can reveal valuable insights into information needs and business processes.
For many retailers, an internal answer engine is a practical first step into the world of generative AI.
“Many retailers first think of marketing when it comes to generative AI. However, the greatest potential lies in customer service.”
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Why customer service is becoming the most important use case
While marketing often dominates the conversation around generative AI, Miya Knights believes the greatest value lies in customer service.
Employees in stores and contact centres need quick access to product information, service details and company knowledge. Generative AI can help make this information instantly available, significantly improving the quality of customer support.
Supporting employees more effectively
The faster employees can find relevant information, the better they can assist customers. This is particularly valuable when dealing with complex product ranges or extensive service offerings.
The technology helps make knowledge more accessible and reduces uncertainty during customer interactions. After all, expert advice is a key part of creating a positive shopping experience.
Making business knowledge available across the organisation
In many retail businesses, information about products, services, stores and processes is spread across multiple systems. As a result, employees often spend valuable time searching for answers.
Generative AI can bring this knowledge together and make it centrally accessible. This creates a knowledge base that can be used immediately during customer interactions.
Answer engines: A practical starting point for generative AI
For retailers looking to gain their first experience with generative AI, Miya Knights recommends a pragmatic approach: building an internal answer engine.
An answer engine is an AI-powered system that draws on company knowledge and provides employees with relevant information in real time. Rather than searching through multiple databases or documents, employees can access the information they need through a single interface.
What are the benefits of answer engines?
Answer engines provide fast access to relevant business knowledge and reduce the time spent searching for information. At the same time, they help ensure that customer enquiries are answered more consistently and accurately.
They also enable organisations to make better use of existing knowledge and support employees more effectively in their day-to-day work.
What information should be included in an answer engine?
The more comprehensive the knowledge base, the greater the benefit. Useful sources include product data, assortment information, service details, store information, company policies, FAQs and internal knowledge bases. The key is ensuring that information is accurate, up to date and easy to access.
Tips for retailers
Start with a specific use case Many organisations feel overwhelmed by the possibilities of generative AI. A clearly defined use case makes it easier to get started and achieve quick wins.
Centralise company knowledge Product, service and business information should be as complete and up to date as possible. The quality of the data has a major impact on the value generated by AI.
Test an internal answer engine Existing AI platforms provide a quick and practical way to explore the potential within your organisation.
Analyse employee questions Using an answer engine can provide valuable insights into knowledge gaps, information needs and opportunities for improvement.
Prioritise customer service Retailers looking for quick results should not focus exclusively on marketing applications. Customer service is often where the greatest benefits can be achieved.
Miya Knights is Publisher of Retail Technology Magazine and a recognised expert in retail technology. She analyses global developments in retail, customer experience and digital innovation, with a particular focus on how retailers can successfully implement new technologies in practice.
Generative AI refers to artificial intelligence that can create content, text, images or responses based on existing data. In retail, it is used for product information, customer service, knowledge management and marketing, among other applications.
Typical use cases include product information, product imagery, customer service, internal knowledge bases and supporting sales teams. Generative AI helps streamline processes and make information available more quickly.
Answer engines provide quick access to business knowledge, reduce time spent searching for information and help employees deliver more informed customer support. They also improve the use of existing data and knowledge.
Store associates can access product information, service details and store information in real time. This improves customer interactions, reduces search times and enhances service quality on the shop floor.
Product data, assortment information, service details, store information, company policies, FAQs and internal knowledge bases form the foundation of an effective answer engine.
A sensible first step is to implement a secure internal AI solution that can access existing company knowledge and support employees in their day-to-day work. An answer engine can provide a practical and low-risk starting point.
