The same question keeps coming up among the teams that run warehouse operations: "Where should we start with automation in the warehouse, and do we have to make a big robot investment?" The question is not new, but the ground under the answer is shifting.
According to research that Gartner published on September 16, 2026, warehousing has reached a tipping point for AI applications. Gartner attributes this to three forces coming into play at once: labor constraints that make automation indispensable, capital models shifting toward lower-risk entry points, and AI and autonomous technologies approaching operational maturity.
In this article, we look at the four trends Gartner has identified, the data for Europe and Türkiye, and the first steps that can be taken without a large investment.
Why now: labor pressure and new warehouse design
Labor pressure also stands out in Gartner's April 2026 prediction. According to the firm, warehouse workers are increasingly unwilling to do manual labor, and because labor costs and supply are also under strain, many organizations will struggle to sustain operations through hiring alone.
In the same study, Gartner predicts that 50% of new warehouses built in developed markets by 2030 will be designed as robot-centric facilities in which humans are "optional." In this model, human labor is no longer the foundation of daily operations and is needed only to manage exceptions.
This prediction concerns newly built facilities; for most existing warehouses, the question is which step to take first within the current setup.
Four trends: a spectrum from software to robots
Gartner positions the four AI trends transforming warehousing along two axes, "action orientation" and "level of intelligence":
- Optimization-focused traditional AI: Demand forecasting, labor planning, route optimization and inventory management applications adapt to changing warehouse conditions using richer real-time data. Gartner stresses that these applications retain their advantages of transparency and repeatability.
- Operations-focused generative AI: Produces standard operating procedures, work instructions, exception-management protocols and decision-support tools from unstructured data.
- Recommending and semi-autonomous agents: Analyze data and either recommend or partially execute multi-step workflows, while human oversight is retained. Task assignment, exception management and resource allocation are typical use cases.
- Physical AI agents: Combine AI, robotics and sensors to automate manual tasks such as picking, packing, sorting and material handling.
This ordering also sheds light on the question of a low-risk start. The first three trends sit mainly in the software and process layer, whereas the fourth requires physical investment, layout changes and integration. Gartner's advice to supply chain leaders points the same way: start with proven use cases such as labor forecasting and inventory slotting, and expand into generative AI and agents where they improve decision quality and efficiency.
Realistic expectations for robotics
Gartner also sounds a note of caution on robot investment. According to the firm, by 2028 the number of companies that take humanoid robot trials beyond the pilot stage will stay below 100, and the number moving to live use in supply chain and manufacturing will not reach 20. Gartner sees multifunctional robots as the more suitable option for most companies that prioritize return on investment, and recommends automation that focuses on specific bottlenecks rather than a general "headcount reduction" goal.
The April 2026 study carries a similar architectural warning: because the in-warehouse robot market is fragmented, most companies will have to use several robot types and an orchestration platform to coordinate them. For this reason, scalable, software-defined platforms are recommended over single-purpose automation.
Data from Europe and Türkiye: logistics is still at an early stage
Official statistics show that AI in the warehouse is still far from the mainstream. According to Eurostat data, among EU enterprises that use AI, use for logistics purposes has the lowest share, at 6.08%. AI for machines that move physically by observing their surroundings and making autonomous decisions was used in only 1.39% of enterprises in 2025, and in 8.55% of large enterprises. The sector with the second-lowest share of enterprises that do not use AI but are considering it is also transportation and storage, at 10.28%.
A similar picture emerges in Türkiye on the data-infrastructure side. According to the 2026 bulletin from TÜİK (the Turkish Statistical Institute), one of the activities with the lowest use of purchased cloud computing services is transportation and storage, at 16.0%. Because the figures cover different scopes and years, they should not be compared one to one; still, the shared message is clear: in a significant part of the sector, the digital foundation is still being laid. We covered the overall prevalence of AI-assisted process automation in our article on European data, and the automation of planning decisions in our article on supply chain planning.
Starting without a large investment: a practical path
The steps below are a general framework derived from Gartner's recommendations; each organization needs to adapt them to its own warehouse structure, product profile and systems.
- Measure the bottleneck. Determine, based on records, which of the steps such as picking, put-away, shipment preparation and counting causes the most problems in terms of time and errors. Define the automation target in terms of the bottleneck, not headcount.
- Get the data in order. Check the consistency of location, stock, order and workforce data in the warehouse management system. If there are manually maintained spreadsheets, connect them to the system first; optimization algorithms give results only as accurate as the data they are fed.
- Start with a proven software scenario. The labor forecasting and inventory slotting optimization that Gartner gives as examples can be tried on the existing process without any robot investment.
- Automate repetitive office work. Screen- and document-based tasks such as order and delivery-note matching, carrier notifications and exception reporting are good candidates for software robots and workflow automation.
- Digitize procedures. Use generative AI, with human approval, to keep work instructions and exception-management texts up to date; every instruction that goes out to the floor should have a clear owner.
- Start agents with authority to recommend. In task assignment or resource allocation, first let the agent only make recommendations; define the approval threshold and exception rules in writing.
- Validate robots with a pilot. When moving to physical automation, run a pilot in a limited area before a full-scale investment; if possible, test the layout in advance with a digital twin and simulation. Prefer structures that can manage different robot types on the same platform over single-purpose systems.
- Monitor the results continuously. Compare productivity, error rate, uptime and number of exceptions before and after the pilot; tie the decision to expand to these measurements.
Conclusion
Gartner's data shows that AI and automation in the warehouse are beginning to move from the trial stage into operational use, while European and Turkish statistics show that most of the sector is still at the start of that road. This picture means a large robot investment is no longer the first step. A measured bottleneck, orderly data, a proven scenario in the software layer and agents that work under human oversight make it possible to move to physical automation on firmer ground.
Why logistics experience matters in warehouse automation. The steps above begin with questions the operation itself has to answer: where the bottleneck really is, whether location and stock data can be trusted, and how a pilot will be measured. One of our consultants brings more than 30 years in operations, logistics and supply chain: at a technology distribution company of roughly one billion dollars in scale, he ran logistics, warehousing, technical service and administrative operations end-to-end, building and leading teams of 100+ people and delivering lasting gains through KPI-driven programs. He has also managed nationwide warehouse and transport operations, large-scale distribution networks, and network and capacity planning at leading retail, logistics and distribution organizations. An industrial engineer, he now brings that supply-chain discipline to SMEs through AI and automation. Meet our consultants.
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