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Augmented reality (AR) can guide warehouse workers through picking tasks with instructions in their field of view, while artificial intelligence (AI) can plan routes, group orders, allocate labor and help logistics teams respond to changing conditions. Used together, they can connect better operational decisions with clearer instructions on the warehouse floor—but the available results are operator-reported examples, not guarantees of industry-wide gains.
How can augmented reality improve warehouse picking?
In vision picking, a worker wears smart glasses that display task information—such as an aisle, item location and quantity—while the worker handles the goods. The aim is to reduce the need to consult paper lists or handheld screens between picks. A system may also use barcode reading, object recognition and indoor navigation to help identify items and guide workers.
What DHL’s pilot showed
DHL reported a vision-picking pilot in Bergen op Zoom, Netherlands, with Ricoh and Ubimax. During the three-week pilot in 2015, 10 order pickers handled more than 20,000 items and fulfilled 9,000 orders. DHL said picking efficiency increased by 25 percent during that trial. That is a company-reported result from one bounded pilot, not a typical or independently validated estimate for other warehouses.
Jan-Willem De Jong, then Business Unit Director Technology at DHL Supply Chain Benelux, described the approach as enabling hands-free picking and increasing productivity. That statement was part of DHL’s announcement of its own pilot, rather than an independent assessment.
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Where AR may fit beyond picking
DHL Trend Research’s 2014 report discussed AR applications in warehouse work, transportation optimization, last-mile delivery and value-added services. These were presented as use cases and a forward-looking outlook; the report does not establish that each was routinely deployed or had proven operational benefits.
The same report estimated that warehousing accounted for about 20 percent of logistics costs, and that picking represented 55 to 65 percent of warehouse operating costs. DHL attributed both estimates to de Koster and colleagues (2006), so they should be read as historical estimates cited in a 2014 report, not current universal cost shares.
How can AI optimize logistics and supply chains?
AI can support planning tasks that are difficult to manage efficiently by hand across many orders, workers, locations and changing priorities. In a warehouse, optimization software can calculate a sequence of work and adjust assignments; across a supply chain, AI analytics may help identify risks and evaluate responses.
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Warehouse routes, orders and labor
DHL described its IDEA software as supporting route optimization, logical order clustering, staff allocation, workload balancing and priority handling. In its first commercial deployments in DHL-operated e-fulfillment warehouses, DHL reported up to 50 percent less employee travel distance and up to 30 percent higher productivity at individual locations. These are company-reported, location-specific “up to” results, not independent benchmarks or a promise of the same outcome elsewhere.
These measures describe different outcomes: less worker travel is not the same as higher productivity, and neither should be combined with the 2015 vision-picking pilot’s result as though the figures came from one system or test.
Disruptions, inventory and capacity
In its September 24, 2026 announcement of Logistics Trend Radar 8.0, DHL Group described AI analytics and agentic AI as emerging capabilities that could help anticipate inventory shortages, reroute around disruptions and adjust transport capacity. This is DHL’s current trend framing, not evidence that autonomous AI is already routine across logistics operators. DHL also highlighted the need to consider workforce skills, safety, governance and employee experience as these capabilities develop.
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How AR and AI work together—and where robotics fits
AR and AI address different parts of a workflow. AI software can recommend what work should happen next, which route to take or how to distribute a workload. AR can show a worker the relevant task instruction at the point of action. That creates a plausible operational pairing, but the cited DHL reports do not establish a combined AR-and-AI system’s measured results.
| Approach | Primary role | Typical worker interaction | Evidence and qualification |
|---|---|---|---|
| AR | Present picking or other task guidance in the worker’s view | Hands-free visual instructions, with a human performing the task | DHL reported a three-week vision-picking pilot result in 2015; it is not an industry-wide benchmark. |
| AI optimization software | Plan routes, cluster orders, balance workloads and allocate staff | Recommendations or task sequences delivered through operational systems | DHL reported “up to” results from first commercial IDEA deployments at individual DHL-operated locations. |
| Robotics | Physically move, sort or manipulate goods, depending on the system | Human-supervised or automated physical workflow | DHL’s AI logistics explainer discusses AI-enabled warehouse robots and sorting, but the evidence here does not establish a comparable performance figure. |
These approaches can overlap, but they are not interchangeable. A warehouse may need better task sequencing without wearable displays, or worker-facing guidance without automated physical handling. The right choice depends on the operational bottleneck, existing equipment and systems, and how much human oversight the task requires.
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What a logistics operation needs before adopting AR or AI
The reported workflows depend on more than buying a device or software license. DHL’s description of vision picking identifies enabling components such as object recognition, barcode reading, indoor navigation and warehouse management system (WMS) integration. AI optimization also needs reliable order, item, location and labor data to produce useful plans.
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Check operational fit
- Task fit: Identify whether the problem is worker guidance, route planning, workload allocation, physical transport or a combination. Match AR to the worker-facing instruction problem, AI software to planning and allocation, and robotics to physical movement or manipulation.
- Data and integration: Confirm that item and location records are accurate and that the proposed system can connect to the WMS and relevant order or labor-planning systems. Poor source data can undermine both recognition and optimization.
- Working conditions: Evaluate task duration, lighting, network coverage, glove and personal protective equipment (PPE) compatibility, device durability, charging needs and exception handling under actual shift conditions.
- People and safety: Involve workers in workflow design and test ergonomics, training needs, safety impacts and acceptance. Establish how workers can challenge or escalate an incorrect instruction or automated assignment.
- Governance: Decide who oversees automated decisions, what happens when recommendations conflict with operational judgment, and how changes to rules or models are managed.
Measure a pilot against the existing workflow
Before a trial, define the baseline and the outcome that matters: for example, pick rate, travel distance, error rate, time per order, or the balance of work across a shift. Track exceptions and training time as well as headline productivity. Compare like-for-like tasks and operating conditions, and distinguish a pilot result from sustained performance after rollout. The DHL figures above do not establish typical cross-operator return on investment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What warehouse smart glasses do—and what the evidence does not establish
Warehouse smart glasses are a category of head-mounted display used to show work instructions or other information while a worker’s hands remain available. DHL’s historical vision-picking pilot named Google Glass and Vuzix M100, and its logistics report described head-mounted displays as part of vision-picking systems. Those references document devices used or discussed in that historical context; they do not establish current model availability, industrial suitability for a full warehouse shift, or an endorsement.
A wearable barcode scanner may be another component in a hands-free workflow, but a scanner or display alone does not provide the route, item data, recognition functions or WMS connection needed for the described system. Current equipment selection requires checking suitability, support and integration for the specific operation.
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What the reported results can—and cannot—tell operators
The concrete performance figures in these examples come from DHL announcements about its own pilot or deployments. They show that AR-guided picking and AI-based warehouse optimization have been applied in operational contexts, but they do not tell another operator what gains to expect, what implementation will cost, or whether the same results will persist at scale. Independent validation and typical cross-operator ROI are not established by these reports.
DHL’s September 2026 Trend Radar announcement quotes Chief Commercial Officer Katja Busch saying that organizations need to understand emerging shifts early and turn them into practical advantages for their business and people. That is a strategic perspective, not a measured finding. For operators, the practical test is whether a carefully scoped workflow improves the specific service, cost or safety outcome they set out to change.
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