Passengers arriving at San José Mineta International Airport’s Terminal B are greeted by a humanoid robot named José. Mounted to a fixed base behind an information desk, José greets travelers and switches to the language they speak, answering questions about flights, baggage and directions.
What engineers behind José are really testing is whether an AI system can stay accurate and responsive inside infrastructure built long before anyone imagined it would need to serve a robot.
Airports are already heavily automated environments. Conveyor systems route baggage, software coordinates gates and departures, and automated ramp systems manage aircraft turnaround. What is changing is the growing use of AI-powered humanoid robots operating directly alongside people.
The humanoid form factor is not simply for novelty or branding. Human-shaped robots can operate inside existing infrastructure with fewer modifications than many purpose-built automation systems would require.
José, the friendly face of Silicon Valley
But fitting in physically is the easier part. Passenger interactions are exactly what make airports difficult environments for public-facing AI systems. Passenger information changes constantly, interactions happen under time pressure, and systems must operate reliably despite noise and connectivity constraints.
This is why the deployment is deliberately constrained. Although José can stand and walk, IntBot, the company behind José, chose to tether the system during the pilot. According to the company, the decision was practical; in a crowded airport environment, a loss of power or battery failure could create obvious safety problems.
San José Mineta (SJC) and Tokyo Haneda Airport (HND) are among the busy hubs trialing robots in crowded terminals, runway aprons and passenger service areas. With San José preparing to host matches during the 2026 FIFA World Cup, the airport’s four-month pilot is also becoming an early test of how AI systems perform during periods of intense international passenger traffic.
The airport says José supports travelers in more than 50 languages, a capability that matters in a region as linguistically diverse as Silicon Valley. “What was really surprising to me is that I expected probably 90% of conversations to be in English,” said IntBot product manager Hannah Wu. “It actually turns out that 25% of interactions are in a language other than English.”
Robot José in SJC. Credits: IntBot
The system combines conversational AI with live flight and airport information, allowing passengers to ask follow-up questions naturally rather than navigating static menus or kiosks.
“Physical agents” orchestrating models across edge and cloud
According to IntBot CTO Sharon Yang, José distributes AI workloads across both edge and cloud infrastructure depending on the task being performed. Rather than relying on a single monolithic model, the system acts as what Yang described as a “physical agent” orchestrating multiple models and tools in real time.
While José can access live flight and airport information in conversations with passengers, the system remains relatively loosely integrated into airport infrastructure. IntBot said the system currently pulls flight status information and floorplan data through APIs rather than operating as a deeply embedded backend system.
The distinction reflects a broader challenge facing physical AI deployments. Airports are complex environments built around legacy systems, rapidly changing information, and human workflows that were never originally designed around autonomous machines.
Mookie Patel, director of aviation at San José Mineta, said the goal was not simply to replace static information systems, but to test whether conversational AI could operate effectively in a live terminal environment.
“Unlike a static kiosk or an app with pre-programmed responses, José can answer follow-up questions, switch languages instantly and personalize the interaction,” Patel said. “For first-time users, the interaction feels very similar to speaking with a customer service agent.”
According to Hannah Wu, around three-quarters of interactions are socially driven rather than task-based, with passengers often approaching José out of curiosity before asking practical questions.
The company previously deployed robots in hotels before moving into airports, partly because multilingual wayfinding and information services offered a practical early use case for public-facing robotics.
“In a lab, there’s just so many edge cases that you can’t prepare for,” Wu said. “In the real world, that’s how we make our products better.”
Japan Airlines Haneda trial
Other airport robotics projects are focusing on labor-intensive work behind the scenes.
Passengers glancing out of the departure lounge windows at Tokyo Haneda Airport may see the familiar choreography of airport ground crews hauling unit load devices (ULDs), the wedge-shaped containers used to move luggage, freight and mail onto aircraft. But among the workers shifting containers onto rolling ramps, one figure may appear a little more metallic than the others.
Japan Airlines (JAL) and GMO AI & Robotics Corporation are beginning what the companies describe as Japan’s first airport demonstration trial involving humanoid robots in ground handling operations.
Unlike the public-facing concierge role taken by José in San José, the Haneda trial focuses on repetitive manual work already shaped around human crews. Initial deployments are concentrating on ULD transfer tasks on the airport ramp, with future phases potentially expanding into baggage loading, cargo handling, cabin cleaning and even operation of ground support equipment.
The distinction matters. While humanoid robots are often framed as futuristic consumer technology, the strongest near-term case for deployment may be in exactly the kind of routine work airports are increasingly struggling to staff.
“While airports appear highly automated and standardized, their back-end operations still rely heavily on human labor and face serious labor shortages,” said Tomohiro Uchida, president of GMO AI & Robotics.
Japan’s aviation sector, like much of the country’s economy, faces growing labor shortages driven by demographic change and increasing tourism demand. Ground handling work combines strenuous conditions with strict safety requirements and operational time pressure, making automation attractive but difficult to implement using conventional industrial robotics.
According to JAL, the goal is not full automation, but gradual workload reduction and productivity gains through systems designed to work alongside human crews. Using robots for physically demanding tasks would “inevitably reduce workers’ burden” and provide “significant benefits to employees,” Yoshiteru Suzuki, president of JAL Ground Service Co., told Kyodo News. He added that some responsibilities, including safety management, would still require human oversight.
A JAL spokesperson said the integration of humanoid robots and automated vehicles could eventually reduce personnel requirements by roughly half in some container loading tasks, contributing to a broader goal of improving productivity 10% by 2030.
But scaling those systems beyond pilot projects will depend on proving they can operate reliably in highly constrained airport environments.
The airport doesn't adapt to the robot
The projects at San José and Haneda reflect two very different visions of airport robotics. What they share is the challenge of deploying AI systems inside environments built around human behavior and day-to-day workflows.
That complexity is driving renewed interest in humanoid systems rather than purpose-built industrial machines.
The attraction of humanoid systems is not necessarily that they outperform conventional automation, but that they may be able to operate inside infrastructure already designed around human movement, tools and procedures. In many cases, adapting robots to human environments may prove easier than redesigning airports around robots.
For now, most deployments remain tightly constrained. José is tethered behind an information desk. The Haneda trial is focused on limited operational tasks under controlled evaluation. Scaling these deployments comes down to one thing: whether the AI behind them can hold a real-time connection to systems that change every second and break expensively when they fail. The integration layer is the actual product.
