Mark Scrivens, Chief Executive Officer, FPT UK; FPT Co., Ltd.
The impact of AI in retail is already becoming a reality with several key factors driving retail transformation. This combines advances in AI technology with the need for retailers to reduce costs through efficiency and meet customer demands for highly personalized experiences when browsing and purchasing. In fact, by 2030, AI revenue in the UK retail market is expected to reach approximately USD 1.68 billion, with an average annual growth rate of 16.7% from 2025 to 2030.
Retail leaders who embrace innovation have a huge opportunity to stay agile, relevant, and competitive with their services, from proactive personalized promotions to conversational virtual assistants and dynamic inventory management.
However, for brands to be successful in implementing AI, they must overcome some key challenges and considerations.
The biggest challenges for retailers implementing AI
For years now, retailers have been advised to leverage data across all channels, but time is running out to prepare for AI. There is a growing divide between retailers with clunky websites that direct consumers to future-ready retailers that use intelligent, immersive technology to increase customer loyalty and sales. 2025 could be the year of reckoning that separates the brands that benefit from AI from those that don’t.
There is also the issue of being stuck in long budget cycles that inhibit investment in AI and have a clear impact on retail stagnation. Because AI relies heavily on accurate and complete data, retailers are under pressure to update legacy systems that are hindering effective data utilization. To be able to explore AI technologies, structured and unstructured data must be enabled.
There are also some potential AI risks to overcome, including poor quality interactions, the risk of data misuse, and lack of meaningful human involvement. To maintain consumer trust, these critical customer service concerns must be alleviated during technology implementation.
It’s important for retail leaders to realize that AI technology alone won’t do the trick, and specialized training is required to achieve the right business outcomes. Advanced machine learning has three main pillars. It’s the talented data experts who can manipulate the algorithms. Accurate and complete data, and methodologies that combine data with human expertise to deliver value.
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Trends in AI utilization in retail industry
Savvy retailers are leading the field and staying ahead of the curve with the latest AI trends. Conversational AI is one of the biggest innovations for automating real-time customer interactions, humanizing chatbots to understand context, tone, and consumer intent. This allows customers to have more natural and engaging interactions with chatbots and virtual assistants in real time. These systems use natural language processing (NLP) to transform conversations into structured data, providing valuable insights into customer expectations from your online store.
By leveraging AI across many business functions, companies can deliver highly personalized experiences, such as customized product recommendations and targeted marketing campaigns, personalized customer support and dynamic pricing strategies.
For example, there are two obvious areas where retailers can avoid misstocks (understock or overstock) and make a positive impression on customers while enabling more accurate size orders. AI-powered inventory management and virtual sizing tools help customers make better size selections, significantly reducing returns and costs while increasing supply chain efficiency.
Retailers are taking things even further by merging AI with retail industry immersive augmented reality (XR) technologies such as VR and AR. This could provide virtual sizing tools for more precise clothing purchases, or AI assistants to help you shop for items to fit specific spaces, such as TVs or large pieces of furniture.
5 AI trends for retail use cases
There are several classic use cases for AI in retail settings, and it is becoming widely accepted as the standard. These include:
- virtual customer service – Clumsy chatbots are no longer acceptable to today’s customers who have experienced more advanced AI-based virtual assistants. Understand customer intent with a more human touch, provide instant responses with over 100 natural voice options, and build customer trust and loyalty.
- training – Generative-AI-based store associate-specific training model helps improve sales capabilities and business efficiency. An intuitive virtual assistant not only streamlines internal staff communication, but also quickly resolves inquiries about processes, compensation and benefits, promotions, product information, and technical support.
- document processing – Retailers generate and store large amounts of data such as documents, emails, and images, so using AI-based solutions to automate the processing of up to thousands of retail documents per hour with high precision increases efficiency. It can support a variety of templates such as invoices, delivery notes, receipts, tax documents, etc.
- inventory management – AI-based inventory management tools allow retailers to optimize inventory management and significantly reduce returns, another big problem. Retailers need to share data across offline and online channels to ensure customer information is shared from browsing to point of sale (PoS).
- customer survey – Customer surveys are conducted regularly in the retail industry. For example, FPT’s AI-enabled CSAT Autocall solution automates customer research for products and services. Get real-time scoring during calls and generate detailed and insightful information to inform your retail strategy.
Align people, data, and methodology
It is interesting for retailers to explore the appropriate use cases for their specific business. However, some additional considerations should be taken into account before implementation. First, retailers must first perform an ethical impact assessment to be aware of potential ethical implications. AI developers should also adopt a user-centered approach to AI software development ethics by involving various stakeholders in the creation of AI systems.
Brands should work closely with data experts to ensure unbiased training data. This means getting used to working with unbiased datasets, ensuring a diversity of data points to increase the reliability of AI models. Partnering with a trusted digital transformation partner can help guide your business through this and other complex challenges in your AI journey.
Especially in the new era of AI in retail, aligning people, data, and methodology is the ideal approach to realizing AI’s potential to improve creativity, efficiency, and revenue.
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