How quickly are embodied AI Systems being adopted?
What percentage of major U.S. warehouses (>100,000 sq ft) will use autonomous mobile robots for inventory management by 2028 and 2031?
Embodied AI refers to systems like autonomous robots that interact with and manipulate the physical world, increasingly handling real-world tasks across manufacturing, healthcare, transportation, and logistics. Understanding the trajectory of embodied AI deployment is important because these systems will collect data from physical environments and take direct physical actions in the world, creating new possibilities for both beneficial uses and potential risks. Embodied AI systems can directly affect critical infrastructure like power plants, government facilities, and populated areas in ways that purely digital systems cannot. The pace of this deployment matters because it determines how much time policymakers have to develop appropriate oversight frameworks and safety measures. Studying warehouse robotics provides insights into these broader deployment patterns and the practical challenges of integrating AI systems into physical-world applications across multiple sectors.
The modern supply chain depends heavily on warehouse operations, vast facilities where products are stored, sorted, and distributed to retailers or directly to consumers. In the United States alone, there are over 10,000 warehouses larger than 100,000 square feet, employing approximately 1.8 million workers who manually locate products, check inventory levels, and move items throughout these facilities.
This labor-intensive system is under pressure. E-commerce growth has created demand for faster, more accurate fulfillment, while warehouse operators face rising labor costs. At the same time, technology has progressed to the point where autonomous mobile robots (AMRs) can now perform many warehouse tasks independently.
AMRs represent some benefits over traditional warehouse automation. Unlike fixed conveyor systems or guided vehicles that follow predetermined paths, AMRs collect data from a variety of sensors and make real-time decisions to navigate while avoiding obstacles and adapting to changing warehouse layouts. They can conduct inventory counts, guide human workers to specific products, and transport items between locations, all without human intervention.
Early adopters demonstrate the technology's potential. Amazon recently deployed its 1-millionth robot and developed a new generative AI foundation model designed to make their entire fleet smarter and more efficient. Walmart uses AMRs in regional distribution centres, while logistics companies like DHL use AMRs for various warehouse operations. These implementations have shown improvements in inventory accuracy, efficiency and reductions in labor costs.
Why it matters
Warehouse robotics represents one of the first large-scale deployments of embodied AI systems, meaning AI that interacts with the physical world. Understanding how quickly these systems are adopted provides insights into the pace of AI progress in real-world applications and broader patterns of AI integration across the economy. The lessons learned about adoption barriers, implementation challenges, and safety considerations in warehouses could also help predict and prepare for similar AI deployments in other sectors, while showing how technological capabilities translate into economic impact.
Ideal Candidate
Interested in technology and policy - no prior expertise required
Motivated to learn quickly and comfortable tackling unfamiliar topics
Research methods: Gather and analyse information from industry sources and market data
Forecasting: Build predictions using available data and identify key uncertainties
Policy analysis: Connect technology trends to policy implications
Clear writing: Present technical findings for non-expert audiences
Mentor: Aquila is a Research Project Manager at GovAI, ensuring the organisation's initiatives and research programs run smoothly. She works closely with the Director of Research and Policy to execute strategic priorities and ensure programs support the research mission. Before joining GovAI, she worked as an engineer on major infrastructure projects. She holds BA and MEng from the University of Cambridge.
