How JD.com Is Transforming Business with Physical AI
AI is evolving from a technology that helps analyze data into one that can drive real-world operations. Physical AI is opening up new opportunities for organizations to improve operational efficiency, overcome resource limitations, and gain a new competitive advantage.
A warehouse that can systematically manage massive volumes of orders. Robots handle product movement and sorting, autonomous vehicles transport parcels, while AI analyzes real-time data to optimize routes and allocate resources according to constantly changing conditions.
What makes this particularly interesting is that these systems do not operate independently. Instead, they are connected as part of an integrated operational process that can perceive, analyze, and take action in the real world.
This is the concept behind Physical AI, a technology that is expanding AI beyond the digital world and into the physical environment. It could become another important factor shaping how businesses compete in the future.
JD.com, one of China's leading technology and e-commerce companies, announced its Physical AI Acceleration Plan in September 2026. The plan sets a five-year target for JD Logistics to deploy 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones.
Although these figures represent future targets, they reflect the growing attention Physical AI is receiving and how it could play a bigger role in business than many might expect.
When AI Moves Beyond the Screen
Until now, we have been familiar with AI as a tool for processing data and supporting decision-making. However, turning those insights and recommendations into action has still required people or other systems to carry out the work.
Physical AI is changing this by bringing AI together with devices and machines that can perceive and respond to their physical surroundings. These include robots, autonomous vehicles, and intelligent control systems.
Behind these capabilities is the integration of several technologies. Computer Vision and Sensors enable systems to see and perceive their surroundings. AI Models analyze information and plan what actions to take, while Robotics and Control Systems allow AI to translate decisions into real-world operations.
However, enabling AI to operate effectively in the physical world is not easy. Real-world environments are complex and constantly changing. Systems must be able to handle unexpected situations and work appropriately with other devices and equipment.
The Impact of Physical AI Across Industries
While JD.com provides a clear example of how Physical AI can be applied in Logistics, its potential extends far beyond this industry. In fact, the technology has opportunities to be applied across many other sectors.
Manufacturing: Physical AI can work alongside robots and machinery to support product quality inspections, component handling, and production-line operations. By using data from Sensors and Computer Vision, these systems can help detect abnormalities and respond to different situations.This can help reduce errors in production processes, improve quality consistency, and reduce the workload of employees performing repetitive tasks.
Healthcare: Robots can support hospital operations by transporting medical supplies and equipment or helping manage medication inventory. This can reduce the workload associated with internal Logistics and give healthcare professionals more time to focus on patient care. Of course, any implementation must continue to prioritize patient safety and appropriate supervision by medical professionals.
Retail: Retail businesses can use automation systems to monitor inventory, organize products, and replenish store shelves. By connecting in-store data with warehouse operations and planning systems, businesses can improve inventory management, reduce stockouts, and make better use of available resources.
Across all these industries, the common opportunity is that Physical AI can help manage tasks that involve perception, decision-making, and physical actions. However, choosing the right technology should start with understanding each business's challenges—not simply adopting it because it is a new trend.
What It Takes to Bring Physical AI into the Real World
As promising as Physical AI may seem, adopting it in real-world operations does not start with simply purchasing robots or selecting the most advanced AI Model. No matter how capable the technology is, investments may fail to deliver the expected results if data is not ready, systems are not connected, or there are no clear approaches to managing risks.
There are four key areas organizations should consider:
Data Foundation: Physical AI relies on data from multiple sources, including Sensors, Cameras, warehouse systems, and various operational platforms. This data must be high-quality, interconnected, and sufficiently up to date to support decision-making. If the information used by the system is inaccurate or incomplete, it can directly affect how the system perceives its surroundings and performs tasks.
AI & Computing Infrastructure: Applications that need to respond to changing situations in real time require suitable infrastructure across Computing, Cloud, Edge Computing, and Network Connectivity. Organizations should therefore assess whether their existing systems can support the processing requirements and connectivity needed between devices.
Workflow & System Integration: Physical AI can deliver better results when it is connected to the systems businesses already use, such as Enterprise Resource Planning (ERP), Warehouse Management Systems (WMS), and Transportation Management Systems (TMS). Connecting these systems allows AI to use information from different operational processes to support continuous decision-making, rather than operating separately in disconnected silos.
AI Governance & Safety: When AI can control equipment or move around in the physical world, errors can have a greater impact on people and property than those occurring in digital environments. Organizations need to establish appropriate governance and safety measures to manage these risks and ensure that AI-powered operations are used responsibly.
Importantly, organizations do not need to start with large-scale investments. Instead, they should begin by identifying processes with existing challenges or opportunities for improvement, then selecting Use Cases that can be tested, measured, and scaled based on actual results.
From Physical AI to Intelligent Operations
JD.com's example shows how AI is moving beyond being a tool for thinking and analyzing data to becoming part of real-world operations. This shift could change how businesses manage resources and carry out their operations in the future.
However, this transformation does not come from having robots or the most advanced AI Models alone. Effective operations also depend on Data, Infrastructure, System Integration, and the right strategic approach.
For organizations looking for opportunities to use AI to improve operational efficiency, preparing these foundations today is an important step that should not be overlooked.
Powering the Future of Intelligent Business Operations
At Sertis, we help organizations turn AI capabilities into measurable Business Impact. From evaluating Business Use Cases and developing AI Strategies to building AI Solutions that connect Data, AI, and Workflow, we help businesses develop solutions that align with their specific needs and operational constraints.
Because entering the era of Physical AI requires more than just smarter technology. It also requires a strong foundation in data, infrastructure, system integration, and governance to support AI adoption in real-world operations.
Ultimately, an organization's competitive advantage may not come from having the most robots, but from knowing how to make AI work effectively alongside people, data, and business processes to create sustainable business value. Source
