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AI can make warehouse operations more efficient by improving demand forecasts, coordinating robots, reducing unnecessary travel, and giving workers better information. These systems can support or change people’s tasks, but they do not guarantee that any particular deployment will preserve every job.
How AI improves warehouse efficiency
Warehouse AI is not a single technology. It can analyze demand and inventory, coordinate robot fleets, surface information on mobile devices, or automate selected physical tasks. The practical question is what work a system handles, how people interact with it, and what evidence supports its claimed results.
1. Forecast demand and plan inventory
Demand-forecasting and inventory tools can help a warehouse anticipate which products will be needed and where to position stock. Better planning may reduce avoidable stock shortages and make more effective use of storage space; it does not eliminate uncertainty in demand or the need for people to manage exceptions.
Amazon Europe describes research conducted with Politecnico di Milano focused on Italian e-commerce companies. In that study, 80% of surveyed companies said they had reassigned employees to higher-value tasks, and 85% reported improved process efficiency and customer satisfaction. These are survey findings within that study’s scope, not proof that AI caused the same outcomes across warehouses generally. Amazon Europe’s account of the study quotes Stefano Perego, VP International Operations at Amazon: “In our fulfillment centres, we use cutting-edge technologies and predictive data analytics to optimise operations—but always with a clear goal: to extend and enhance people’s capabilities, not replace them.”
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2. Coordinate robots and reduce travel
Software can coordinate where mobile robots travel and when they move inventory or containers. If robots take on some transport work, employees may spend less time walking between locations and more time on tasks that require judgment, item handling, or resolving exceptions.
Amazon said in June 2025 that its DeepFleet foundation model is expected to improve robot-fleet travel efficiency by 10%. That figure is Amazon’s stated expected improvement, not an independently verified result. It describes the company’s system and should not be read as a typical gain for other warehouses. Amazon’s announcement about DeepFleet and its robot fleet explains the company’s claim.
3. Bring inventory to workstations and assist with strenuous tasks
Goods-to-person systems move inventory to a workstation so workers do not need to travel to every storage location. Other robotic systems can take on repetitive physical actions such as sorting, lifting, or placing packages. People still handle work that depends on item-level decisions, oversight, or responses to unusual conditions.
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Amazon describes these uses as part of its warehouse operations and reports its workplace safety performance in a company article. Such descriptions show how the company says its systems are used; they do not establish that every installation reduces injury risk. Outcomes depend on the equipment, workflow, training, and safety practices at each site. Amazon’s 2024 workplace safety report discusses its safety approach and robotics.
4. Give workers better information through devices and AI applications
Warehouse mobile devices can help employees identify inventory, complete quality checks, and access operational information where the work happens. AI applications may add decision support to those tools, but a mobile device is not automatically an AI system: capabilities depend on the software and the data connected to it.
Zebra Technologies reported in February 2025 that 63% of surveyed warehouse leaders planned to implement AI software and augmented reality within five years. This measures stated plans, not current adoption or demonstrated improvements in worker performance. Zebra also reported rising injury concerns among surveyed frontline workers, which is a reason to assess safety and ergonomics alongside efficiency. Zebra’s survey announcement describes its findings.
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5. Pair mobile robots with human pickers
Autonomous mobile robots can travel through a facility alongside workers and assist with moving orders or inventory during picking. This is a collaborative arrangement: robots handle movement, while employees pick items and address work that the automated system cannot complete on its own.
DHL Supply Chain reported in June 2024 that its deployment using Locus Robotics autonomous mobile robots had passed 500 million picks. That is a reported cumulative deployment milestone, not a productivity percentage or a result that can be compared directly with Amazon’s travel-efficiency projection. DHL’s release quotes Chief Information Officer Sally Miller saying the milestone underscored “the improved productivity, accuracy, and employee ergonomics” experienced across the network; this is the company’s characterization of its deployment. DHL’s report on the 500 million-pick milestone provides the deployment context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Will warehouse robots replace workers?
There is no basis in these examples for promising that AI will never replace a worker or preserve every role. Automation can change the number and type of tasks people perform, and employment effects depend on a facility’s decisions and operating needs. The more defensible point is that many systems are designed to work alongside people or take over particular tasks, while creating demand for oversight, exception handling, maintenance, and technical support.
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The Associated Press reported in November 2024 that some Amazon systems were deployed at scale while others were still being tested or had not been rolled out broadly. It also described the need for human recovery when real-world disruptions occur. Julie Mitchell, director of Amazon’s robotic sortation technologies, told the AP: “It’s not a matter of having robotics take over but making it one system of humans and robotics working together to accomplish the goal of shipping the product.” The AP’s reporting on Amazon warehouse robotics adds context on implementation and human involvement.
Amazon Europe also reported that 20,000 employees in its European last-mile network were trained on new technologies in 2024, and that reliability, maintenance, and engineering roles in Europe increased 50% over two years. These are company-reported workforce figures; they illustrate training and role changes at Amazon, not a guarantee of the employment outcome at another employer.
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How to assess a warehouse AI proposal
Efficiency claims are only useful when they specify the task, outcome, and setting. For a proposed system, check:
- Task: Does it forecast demand, move inventory, guide picking, or automate a physical action?
- Human involvement: Which decisions and exceptions remain with workers, and who intervenes when the system fails?
- Integration: Does it connect reliably to the warehouse’s inventory and operational systems?
- Evidence: Is the figure a company projection, a survey response, a deployment milestone, or an independently measured outcome?
- Safety and ergonomics: Has the workflow been evaluated for the actual tasks and site conditions?
- Operational readiness: Is the system broadly deployed or still being tested, and what training and maintenance will it require?
For example, a survey of leaders’ plans, a projected improvement in robot travel, and a cumulative pick count measure different things. They should not be ranked as if they came from one controlled comparison. Gartner’s 2025 supply-chain survey found desk-based employees reported saving 4.11 hours weekly and 1.5 hours weekly per team member, while also finding no correlation with improved output or work quality. Those figures concern desk-based work across supply-chain organizations, not warehouse-floor productivity. Gartner’s survey announcement sets out that scope.
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