Warehouses are no longer simply static storage facilities, they’ve become dynamic, intelligent nodes in supply-chains. Fuelled by advances in machine-learning, robotics, sensor networks and real-time data analytics, artificial intelligence is emerging as a key operational lever.
From automating order-picking to forecasting demand and orchestrating human-robot workflows, AI is driving measurable change in warehouse performance.
Industry research shows that AI and automation are set to play an increasing role in the warehousing sector: a recent report by Zebra Technologies noted that in India and the Asia-Pacific region, 63% of warehouse operators plan to implement AI-driven software within the next five years.
Similarly, wider studies highlight how AI-enabled systems can boost accuracy, throughput and cost efficiency in warehouses.
Robotic Picking and Goods-to-Person Workflows
The collaboration between DSV and Locus Robotics is a stand out example. DSV has implemented Locus’s AI-powered robot-as-a-service (RaaS) solution to support a large-scale fulfilment operation, dynamically routing AMRs (autonomous mobile robots) using the LocusONE platform. The system optimised travel paths, task-assignment and robot/human coordination.
Separately, robotics with embedded AI vision-systems are now being used to recognise items, avoid obstacles, optimise picking sequences and adjust to changing layouts. Robots from Exotec integrate AI to detect humans/obstacles in real time and adapt motion accordingly.
The result: shorter travel distances for pickers, reduced idle time for robots, fewer picking errors, and higher throughput.
Demand Forecasting, Inventory Optimisation and Predictive Maintenance
AI’s value isn’t confined to robots. Warehouse-management systems powered by AI are now forecasting demand, optimising inventory placement and scheduling maintenance on conveyors, forklifts and AS/RS (automated storage/retrieval) systems.
For instance, AI agents help track stock levels in real time, flag when replenishment is needed, and coordinate equipment maintenance before failures occur - reducing downtime.
Digital-twin simulation of warehouse capacity enables optimisation of space, equipment and allocation such that capacity increased without adding real estate. These capabilities mean fewer stock-outs, lower holding costs, less downtime and more agility.
Scaling AI in High-Growth Markets
The AI growth story in the Asia-Pacific region is particularly strong. One Indian e-commerce/retail logistics provider deployed AI-based visual surveillance across its warehouse to monitor labour productivity, truck-loading and unloading efficiency and vendor times. Analysis of the collected data resulted in improved visibility, reduced waiting times and improved throughput.
The Middle East is increasingly adopting AI in warehousing and logistics. A region-wide study showed that AI-enabled smart warehousing is advancing in take-up thanks to rising e-commerce, digital infrastructure investment and government initiatives.
Catching up with China, Singapore and Hong Kong, AI-driven sorting and robotics are also rapidly getting integrated into fulfilment centres across India, Vietnam and other South Asian countries where they handle high volumes and variable items.
These deployments illustrate how AI is not just being used in mature Western markets, it’s also being steadily deployed in rapid-growth economies to manage variable demand, labour constraints and infrastructural limitations.
What This Means in Practice
Warehouse operators now have three clear levers of AI value:
Workflow orchestration: AI coordinates human and machine tasks, reducing idle time and errors.
Data-driven management: Real-time visibility, forecasting, and predictive analytics improve decision-making and responsiveness.
Scalability and agility: Especially in high-growth markets, AI helps cope with surges, variation and infrastructure limitations.
From a business perspective, operators embracing AI can expect shorter lead times, improved accuracy, lower labour intensity per unit shipped and improved asset utilisation. In a competitive e-commerce or omnichannel world, that can translate into faster delivery, better service and lower cost-per-order.
Challenges and Considerations
Deploying AI in warehouses is far from trivial. Key hurdles include integration of legacy warehouse management and ERP systems, ensuring quality of data feeds, managing change in workforce and workflows, and ensuring cybersecurity and system robustness.
With heavy initial investment, the ROI depends heavily on volume, variation, and intelligent application of the tools - smaller operations may find the cost harder to justify unless scaled effectively.
Generative-AI Applications
In the years ahead, we are likely to see more generative-AI applications in warehouse design including layout, slotting, task assignment, more collaborative robots - or cobots - working alongside humans, and tighter real-time orchestration across the supply network and not just within one warehouse. The warehouse of the future may not just respond to orders, it will anticipate them.
In sum, AI’s role in warehouse operations has shifted from experimental to foundational. Organisations that combine robotics, analytics and human-machine workflows are gaining the fastest advantage and those that delay risk falling behind on speed, cost and service levels. The operational baseline is shifting; smart warehouses are now becoming the norm rather than the exception.