In air cargo operations—where misrouted freight, tight turnarounds, and unpredictable delays are part of daily life—full automation still struggles to deliver. While the industry races towards digitisation, the next phase of innovation will not come from replacing human labour, but from supporting it more intelligently.

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“Technology can help businesses stay competitive,” says Stefan Lampa, CEO of ProGlove, “but not every process benefits from full automation, and not every environment is ready for it. Warehouses and airside operations are full of edge cases—unexpected disruptions, last-minute changes, irregular items, and non-linear workflows. In those moments, it is still human judgment that keeps things running smoothly.”

The logistics sector is flooded with tech promising speed and efficiency, but as Lampa sees it, many of these tools fail because they do not reflect how people actually work.

“Robots and other AI-driven technologies are built for predictability,” he says. “They excel at repetitive tasks but struggle in situations that demand any form of intuition.”

Instead of replacing workers, the real opportunity lies in removing friction from their roles.

“The role of the human workforce is evolving. Workers now have more capacity to focus on managing complexity, making quick decisions, and responding to unexpected changes.”

This shift requires tools designed to support—not overtake—human capability. “Aviation logistics environments are fast-moving and full of unpredictability,” he says. “Human-centric technologies help build resilience. They enable workers to adapt and respond quickly without having to fight the tools they are using.”

One clear example of this approach in action is ProGlove’s work with Talma Airport Services. Initially, Talma used phone-based scanners at its 47 Latin American airports, but the devices created new challenges.

“Phones were cumbersome to handle alongside luggage,” Lampa recalls. “They slowed workers down and added unnecessary strain in environments that already demand a lot from frontline teams.”

Introducing wearable scanners—worn on the back of the hand—changed everything. “With these devices, workers could scan luggage as part of their natural movement, completely hands-free,” Lampa says. “They do not have to stop and break their flow.”

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The results were immediate. Scanning time for 100 suitcases dropped from 180 seconds to 100—a 45 percent improvement in efficiency.

“Removing the need to constantly pick up and put down a device helped reduce worker fatigue,” Lampa says. “The built-in haptic feedback also meant workers did not have to second-guess whether a scan was successful, which made the whole process feel smoother and more intuitive.”

Supporting performance without disruption

Wearables are not just faster—they are also smarter. Many now feed real-time performance data into centralised dashboards, giving managers a live view of productivity. “This can include picking times, error frequency, task duration, and more,” Lampa explains. “These data points allow managers to make immediate workflow adjustments.”

That kind of responsiveness is essential in environments like air cargo, where small delays quickly cascade. “If one zone in a cargo facility is consistently slower than others, scan data can pinpoint bottlenecks and help shift personnel or revise processes before delays escalate.”

With ongoing labour shortages, tools that protect productivity without adding stress are increasingly critical. “The most effective technologies will be those that are easy to adopt, require minimal training, and offer immediate results,” Lampa says. “Wearables and other ergonomic tools are already proving essential. These tools allow workers to maintain high levels of productivity without burnout.”

The broader shift, Lampa says, is moving from reactive firefighting to continuous, data-driven optimisation—a mindset where workers and tech function as a unit.

AI as a companion, not a replacement

While robotics and AI are gradually taking on more physical and analytical tasks, Lampa is clear: collaboration, not autonomy, is the goal.

“I expect to see robotics and AI become a much more natural part of everyday operations—but not in a way that replaces people,” he says. “Robotics will take on the heavy lifting and repetitive tasks, making the physical demands of the job a little easier. At the same time, we will see AI working in the background—flagging anomalies, prioritising tasks, and fine-tuning routes—so workers can stay focused on what matters most.”

The biggest breakthrough, he predicts, will come from delivering insights at the point of need.

“Whether it is through a wearable or a heads-up display, the goal is to provide real-time insights without forcing people to stop what they are doing or dig through layers of data.”

This philosophy already shapes ProGlove’s technology ecosystem. For example, the company uses Voxel—an AI-powered safety and performance system—to track incidents such as extended open door durations or cargo spills. The results? A 76 percent reduction in open door time and a 46 percent drop in cargo spills across deployments.

However, Lampa is cautious about AI hype.

“Too often, businesses invest in new tools just to keep pace with innovation, rather than focusing on what they actually want to achieve,” he says. “Technology should always serve a defined purpose. Without that clarity, even well-intentioned automation projects can end up disrupting workflows rather than improving them.”

He is also wary of over-automation. “It is tempting to chase efficiency by automating entire processes, but this approach overlooks the complexity and variability that humans bring to the table,” Lampa warns. “Workers are then left to create their own workarounds, often reintroducing the very inefficiencies the technology was supposed to eliminate.”

Still, Lampa is excited about what is coming next. “We are starting to see wearables that learn from how people move, AI systems that adapt in real time to changing conditions, and interfaces that feel more intuitive through gestures or voice commands.”

There is interest too in more advanced models like Google DeepMind’s Gemini, which aim to embed reasoning into machines.

“But if I am honest,” Lampa says, “we are still a long way off from anything that matches human intuition or decision-making. Especially in industries like aviation and logistics, where every day throws up something unexpected, human judgment is irreplaceable.”

As automation deepens across logistics, the competitive edge will belong not to those who automate the fastest, but to those who design for the worker.

“The future of aviation technology is not about trying to copy or replace what people do,” Lampa concludes. “It is about making technology a natural extension of the worker.”