UK operators rapidly scale up AI deployment in logistics

UK interest in artificial intelligence in logistics has reached its highest recorded level, according to Google Trends data, as operators shift from pilot projects to live deployments.

The search term "AI in logistics" hit a peak index score of 100 in the week of 18 to 25 January, up 156% on early November 2025. The data suggests sustained research and evaluation rather than a short-lived spike.

The increase comes as companies across retail, manufacturing and third-party logistics look for ways to manage more complex supply chains. Labour shortages and the growth of eCommerce fulfilment have added pressure on warehouse and transport operations.

Market forecasts cited by Advanced Supply Chain put the global AI logistics market at USD $26.33 billion in 2025, rising to USD $38.68 billion in 2026. That implies a compound annual growth rate of 46.9%.

From pilots

Advanced Supply Chain, which operates warehousing and fulfilment services, has seen a shift in how UK operators discuss AI: away from exploratory conversations and towards delivery plans tied to business outcomes.

Stuart Greenfield, UK & European Sales Director at Advanced Supply Chain, said: "Across the past year, conversations have moved from exploration to execution. What were once proof-of-concept discussions are now centred on integration timelines and measurable return on investment."

Interest has also broadened beyond data analysis. More companies now expect software to influence decisions across planning and operations, particularly where small delays can cascade into missed delivery windows or higher costs.

Operational uses

In logistics, AI is often discussed in relation to demand forecasting, transport planning and inventory management. These areas generate large volumes of historical and real-time data, while operators also face volatile demand and frequent exceptions such as late inbound deliveries or stock discrepancies.

Greenfield said: "AI is now not just analysing data, it's making decisions and preventing problems. We're seeing AI support predictive demand forecasting, automated exception handling, real-time route planning, intelligent inventory balancing and more accurate planning overall."

Wider adoption also reflects a change in warehouse automation strategies. Many businesses have taken a cautious approach in recent years, testing robotics and automation in contained settings before expanding. The focus is now increasingly on systems that can scale up and down in response to peak periods and shifting order profiles.

Automation mix

The company points to growing interest in modular approaches, including autonomous mobile robots, automated storage and retrieval systems, and mixed human-robot operations. These systems are becoming more common in sectors such as grocery, general merchandise and apparel, where order volumes can fluctuate sharply around promotions and seasonal events.

Modularity also reflects practical constraints. Warehouses often operate in legacy buildings with limited space and varied racking configurations. Labour availability can differ by region and shift pattern. Implementation timelines matter, particularly for retailers running continuous operations that cannot accommodate long shutdowns.

At the same time, many supply chains have become more fragmented. Businesses may run multiple fulfilment sites, local delivery hubs and returns centres. Inventory is increasingly distributed across networks rather than held in a single national warehouse.

Greenfield expects the next stage of adoption to take a broader view. "We expect the next phase of adoption to focus on cross-site optimisation rather than single-facility automation as retailers and manufacturers manage increasingly fragmented fulfilment networks," he said.

Rising UK search interest suggests more decision-makers are assessing how AI could fit into existing transport management systems, warehouse management systems and planning tools. It also points to a growing audience evaluating costs, data readiness and change management as deployments move into day-to-day operations.