The Error of Our Ways: Why Safety Needs to Reexamine Technology, Performance Metrics and Perfectionism

In any setting where safety and security are important, humans are regarded as the weakest link. That’s as true for cybersecurity as it is for busy construction sites. But they’re also the most valuable link.

For decades, workplace safety programs have been designed around the assumption that humans will make mistakes, and that the solution is to make them “better” — more training, more reminders, more alerts and more rules. When incidents do occur, they are often dissected to find the moment a human being failed. From there, the prescription is usually more of the same. Yet serious industrial accidents remain frequent, even in environments with the strictest safety protocols. We need to ask ourselves why that is.

What if the fixation on human error is actually a misdiagnosis? Seen this way, we are focused on treating symptoms rather than addressing the root cause. We are assuming that perfect vigilance is a realistic, sustainable state for anyone working a 10-hour shift in a complex, high-stakes environment.

Roughly 40% of the U.S. construction workforce experiences high-level fatigue on a regular basis, according to a 2022 report. It’s tempting to attribute this to long hours or the natural result of physical labor, but what if the current solution is actually part of the problem?

Modern industrial sites are noisy, fast-moving and cognitively demanding. Operators have to manage not only the immediate task in front of them but also a stream of peripheral hazards, from sudden pedestrian movements to unpredictable equipment behavior.

Fatigue, sensory overload and split-second decision-making are part of the job. Even the most experienced operators cannot maintain peak alertness for every moment of every shift. Expecting them to do so is like asking drivers to keep their eyes on every mirror, every gauge and every inch of the road ahead without blinking for hours at a time. If safety systems are designed with the assumption of perfect human performance, they will fail in the real world.

According to a 2024 research paper, repetitive alerts and false alarms are one of the leading causes of distraction and can result in the cry wolf effect, where alerts are simply dismissed as untrustworthy. As further evidence, a 2022 study that focused exclusively on the mining sector found that consistent exposure to noise and audible alerts can induce auditory fatigue and is a contributing factor to at least 20% of site incidents.

Safety technology has typically been reactive, meaning we wait for an incident to occur rather than preventing them outright. This is akin to a referee waiting for a foul before blowing his whistle to stop game play. Instead, we need to adopt a more proactive approach. We should treat safety technology as a co-pilot that scans, assesses and acts alongside the humans at the controls—supporting them instead of interrupting them.

The Limits of Human Vigilance

Even in ideal conditions, the human attention span has limits. Cognitive science shows that our ability to maintain situational awareness fades over time, particularly in repetitive or high-stimulus environments.

On an industrial site, those environments are the norm: a mix of moving equipment, unpredictable human behavior, shifting weather and, in many cases, constant background noise. Operators are expected to keep track of their immediate task, their surroundings and the movements of others — all while filtering out irrelevant stimuli and responding instantly to the relevant ones.

And that doesn’t even take into account the fact that operators need to be able to hear (with any ear protection and hearing aids or other assisted devices) and comprehend (which is no small feat for workforces that can be dispersed across shifts and locations, multilingual and intergenerational with varied education levels, learning types and any number of learning disorders or mental health disorders).

It’s an impossible balancing act, and one that grows harder with every hour on the job. The notion that accidents can be eradicated simply by demanding more focus or discipline from workers overlooks the physiological limits of our attention, and it underestimates the impact of environmental factors that quietly but steadily degrade it.

When Safety is Part of the Problem

The prevailing safety model in many industrial settings treats incidents as individual failings. When something goes wrong, the default response is to retrain the operator, tighten procedures or introduce new compliance checks. While well-intentioned, this approach assumes that errors are purely the result of lapses in personal responsibility, and that they can be eliminated through discipline or education alone.

In reality, the majority of modern industrial accidents occur in environments where workers are already highly trained and fully aware of the risks. The individuals working on these sites aren’t amateurs; they’re professionals who have completed the necessary training and qualifications to perform their jobs (e.g., OSHA’s 40-hour HAZWOPER training). These individuals are not the problem. Rather, it’s the environment itself—and the passive safety technology that has been deployed.

These environments lack a system designed to catch the things human beings inevitably miss while also filtering out false flags. Alarm systems that trigger at the slightest movement or misread harmless objects, such as identifying stray cones as pedestrians, quickly become background noise, leading operators to ignore them or disable them altogether. The very technology designed to protect workers thus becomes part of the problem because it’s not accurate enough. It works on a better safe than sorry principle where anything that could be a risk is flagged, instead of being able to identify risks with certainty.

Worse still, some of these legacy safety systems act as a judge and jury by shutting down operations or recording and reporting perceived mistakes. This does nothing but foster distrust between humans and machines, prompting workarounds that further undermine safety. The net effect is a reactive, punitive culture that fails to adapt to the realities of industrial work, leaving operators to shoulder the full burden of hazard detection and accident prevention—often in spite of some safety systems.

The Need for Co-pilots, not Overseers

I firmly believe that people are employers’ most valuable asset. Rather than treating them as weak links, employers should acknowledge and reaffirm their value by incorporating technology that extends workers’ perception, reduces their cognitive load, and intervenes only when there is a real risk of injury or threat to life.

What’s changed in recent years is not the idea of pedestrian detection, but the underlying architecture that finally makes it reliable. Older approaches depended on single-lens (monocular) cameras, ultrasonic sensors, or tags and beacons worn by pedestrians—all of which struggled in the real conditions that define industrial sites. A single camera can’t judge depth, which means distance and speed estimates are guesswork. Tag-based systems only work when every person consistently wears a device. And traditional machine vision setups often need cloud processing, introducing the challenge of latency and making them vulnerable to connectivity gaps.

Something called stereoscopic vision removes those constraints. By using a matched pair of industrial-grade cameras, the system can perceive depth the way human eyes do, calculating distance, trajectory and relative movement with precision. That capability can be paired with an on-board neural processing unit (NPU) inside a sealed industrial enclosure, which means all the calculations happen directly on the vehicle. This edge-based design ensures that alerts are based on a real-time interpretation of the environment, rather than a delayed or compressed video that must be sent to the cloud and back. It also eliminates any dependence on external networks, which is critical in warehouses, yards, mines or other work environments where connectivity is often uneven or unreliable.

Another major leap is the training data behind modern AI models. Rather than relying on generic datasets, today’s systems are trained on millions of hours of footage from real industrial environments, including variable lighting, dust, steam, reflective surfaces, and the unpredictable ways pedestrians actually move around machinery. This is why current-generation systems succeed where previous ones failed; they can distinguish a pedestrian from a shadow, a cone or a piece of debris as well as detect partial occlusions, such as someone emerging from behind a pallet or crossing between racks.

Advances in stereoscopic machine vision, edge-based AI detection, and embedded telematics now make it possible to monitor complex industrial environments without relying solely on human observation. These 3D, real-time systems can identify pedestrians and moving equipment with high accuracy, filter out irrelevant motion and even anticipate collision risks before they become imminent. By processing—and acting on—this information locally at the edge, they avoid the latency of cloud-based systems and ensure alerts or interventions happen in milliseconds, not seconds.

When it comes to safety systems, design matters just as much as capability. The most effective safety systems don’t compete with the operator’s focus or demand constant interpretation. Instead, they use intuitive cues (e.g, discreet LEDs, directional audio or targeted vehicle responses) to communicate only what’s necessary—and only when it’s necessary. By minimizing false positives, these systems protect workers’ trust in the system and ensure that every alert has weight. Predictive maintenance features can also be integrated, helping teams address mechanical issues before they escalate into safety hazards.

Conclusion

Once we shed the myth of the perfect operator, a different picture of the future comes into focus. The operator of tomorrow isn’t someone expected to sustain impossible levels of vigilance. They’re a skilled professional who is supported by systems that recognize the limits of human attention and work to extend it.

As stereoscopic vision, edge AI and smarter design become the norm, we move toward workplaces where people aren’t punished for being human; rather, they are protected because of it. Altogether, these advances in technology should create a work environment where confidence replaces anxiety and where attention can be placed on the job rather than the constant fear of missing something.

That’s the real benefit here: A future where technology absorbs the cognitive load that used to fall entirely on the operator, and where safety becomes a shared responsibility between human judgement and machine precision.

In this way, technology becomes a true co-pilot: scanning the environment continuously, stepping in when human capacity is stretched, and doing so in a way that complements (rather than interrupts) the flow of work.