Retail’s AI Turning Point: Signals from a Year of Experimentation

Source: Retail Dive
Date: 23rd December 2025

Despite lagging behind other industries, retailers accelerated AI adoption in 2025, reshaping consumer behaviour, operations and supply chain expectations.

Retail saw an unprecedented volume of artificial intelligence activity in 2025, as companies across mass retail, apparel and e-commerce tested new applications both internally and in customer-facing environments. While adoption remains uneven, the year marked a clear shift from theoretical interest toward operational pilots and real-world deployment.

Compared with sectors such as finance and telecommunications, retail continues to trail in AI maturity. The slower pace is largely attributed to retail’s complex physical operations and thinner margins, which delay returns on investment. Nevertheless, the scale of potential AI use cases, spanning customer experience, pricing, workforce management and supply chains, suggests this gap is unlikely to persist indefinitely.

One of the most disruptive developments in 2025 was the sharp rise in AI-driven online shopping activity. Traffic originating from AI-powered research and recommendation tools surged during peak retail periods, signalling a shift in how consumers discover and evaluate products. Although the overall share of traffic remains relatively small, its rapid growth has raised concerns about retailers losing control over customer journeys and demand signals.

In response, larger retailers increasingly pursued a dual strategy: developing proprietary AI capabilities while also integrating with external AI platforms. This approach reflects a recognition that ignoring AI intermediaries could erode brand influence, pricing power and visibility across digital channels. Smaller retailers, however, face capital and capability constraints that may widen competitive divides.

While AI adoption in retail has yet to reach full scale, 2025 established AI as a permanent strategic pillar rather than a short-term innovation trend. The next phase will focus less on experimentation and more on integration, embedding AI into demand forecasting, inventory planning and omnichannel fulfilment as retailers adapt to a more algorithm-driven marketplace.

AI-driven retail demand signals are becoming faster and less predictable, increasing pressure on shippers to support shorter lead times, flexible capacity and more responsive fulfilment models. Improved data integration with retail clients will become critical.

As AI influences purchasing decisions and accelerates demand swings, importers may face greater volatility in order patterns. This raises the importance of agile sourcing, dynamic inventory strategies and enhanced visibility across inbound supply chains.

Exporters supplying retail markets will need to adapt to AI-influenced demand forecasting and more frequent replenishment cycles. Those able to provide real-time data, flexible production runs and closer collaboration with downstream partners will be better positioned as AI reshapes retail planning.