AMD circulated their 2025 Retail white paper during the NRF ’26 (National Retail Federation) event held in New York, from 11 to 13 January this year, and it can be found here.
It is titled: ” Retail’s AI Revolution: Enhancing Customer Experience and Efficiency” and discusses retail transformation with AI, modern technology, improved customer engagement and data-driven insights.
Retailers are facing mounting pressure from shifting consumer behaviour, staffing challenges and supply chain complexities, all the while being challenged to control costs and improve efficiency. AMD explores how cost-effective AI and data-driven solutions, enabled by AMD’s end-to-end portfolio spanning data centre, cloud and edge, can help retailers modernise IT infrastructure, unlock operational insights, and deliver better customer experiences without blowing tight budgets.
Emerging AI usage in Retail
AMD’s white paper cites a number of AI retail use cases, on the horizon, and already in operation:
• Computer Vision
which will be used to track customer behaviour and offer recommendations and discounts. AI-enhanced cameras and sensors in stores can monitor customer movements, product interactions, and shopping patterns to provide shoppers with real-time recommendations.
• Hotspot Analysis
Computer vision can track customer movement to identify a store’s pinch-points and high-traffic areas. It can also provide data that allows retailers to negotiate higher slotting fees with their suppliers.
• Automating product detail pages (PDPs) in e-commerce with large language models (LLMs).
LLMs can generate SEO-optimised product descriptions instantaneously for online catalogues, greatly reducing manual efforts and improving sales opportunities in fast-changing retail segments.
• Creating short-form product videos with generative AI.
Using product images and descriptions, retailers can auto generate animated explainer videos, 360-degree product spins, or add voice-overs to narrate product features.
Retailers face significant barriers to adopting data-driven AI, including strict data privacy regulations, security requirements, aging IT infrastructure, and a lack of in-house AI and data science skills. These challenges make it difficult to deploy modern AI solutions such as demand forecasting, fraud detection, and personalised experiences, particularly at scale. To overcome this, retailers need trusted technology partners that combine secure, energy-efficient AI-optimised hardware with a broad ecosystem of retail-focused partners to support compliant, scalable AI implementations.
The paper includes a great case study on a home improvement/DIY retailer, the issues that were faced, and the process and infrastructure that were incorporated into the DIY retailer’s system.
Worth a read.
