The Latent Risk of Automation: Why Human Expertise May Be Withering in the Age of AI

Imagine an artificial intelligence system that has performed with near-perfect reliability for months. It processes complex datasets, generates actionable insights, and provides recommendations that are almost always correct. The employee tasked with reviewing these outputs—the "human in the loop"—has grown accustomed to this consistent accuracy. Over time, that employee has learned a quiet, dangerous lesson: close scrutiny rarely changes the outcome. Then, without warning, the system encounters a scenario that defies its training. The AI makes a critical error or simply halts, unable to proceed. In that moment of failure, the system demands human intervention. The employee, still legally and professionally responsible for the final outcome, is expected to step in and fix the problem.

But in that high-stakes moment, a fundamental question emerges: Does the employee actually know how to do the task anymore?

This scenario is not a hypothetical anxiety; it is a growing concern for management experts who study the intersection of human labor and machine intelligence. Murugan Anandarajan, a professor of management information systems and the academic director of Drexel University’s Center for Applied AI & Business Analytics, has identified this phenomenon as "latent risk." It is the subtle, often invisible possibility that human workers are losing the very capabilities their organizations still rely on them to exercise.

As AI systems become more deeply integrated into the fabric of corporate decision-making, the relationship between the worker and the machine is undergoing a profound shift. We are witnessing a transition where AI continues to perform with high efficiency, while the people overseeing it become less practiced at exercising independent judgment. They become "out of the loop," mentally detached from the processes they are supposedly supervising. The risk remains "latent" precisely because the organization continues to operate under the assumption that the human safety net is intact, while there is little cause—until a crisis occurs—to notice that the human capability to intervene is rapidly weakening.

The concern regarding automation is not entirely new, though the current technological context has introduced unprecedented variables. The concept of latent risk builds upon decades of rigorous research into how human beings respond to automated systems. For example, extensive studies of "automation bias"—the tendency for humans to favor suggestions from automated systems even when those suggestions contradict contradictory evidence—have long warned that our cognitive engagement drops when we delegate tasks to machines. In the past, this bias was often limited to specific, narrow technical tasks. Today, however, what is fundamentally different is the role AI is beginning to play in the organizational hierarchy.

We are moving past simple data entry or routine processing. AI is now assuming larger, more complex parts of the analysis and decision-making architecture that human beings previously performed in their entirety. When a machine handles the heavy lifting of synthesis, strategy, and judgment, the human’s role shifts from an active participant to a passive observer. This shift is problematic because human expertise is a perishable skill. Like a muscle that atrophies without exercise, the ability to analyze a situation, weigh conflicting variables, and make a high-stakes decision requires constant practice.

In many corporate environments, the pressure to maintain efficiency and speed creates a feedback loop that exacerbates this risk. When an AI system consistently produces results that are approved by management, there is little incentive for a human worker to take the time to "re-check" the work. Doing so might even be viewed as an inefficient use of time. Yet, this efficiency comes at the cost of long-term readiness. By minimizing the time spent on critical thinking, employees are inadvertently eroding their ability to catch the "black swan" events—the rare, anomalous situations where the AI’s logic fails because it lacks the nuance, context, or ethical grounding of a human mind.

The danger of this latent risk is that it remains invisible until it is too late. Organizations are often blind to the erosion of their own internal capabilities because the AI is performing so well. If an AI handles 99% of tasks correctly, the 1% failure rate might seem manageable on paper. But if the human staff has become so reliant on the AI that they have lost the ability to navigate that 1% error, the failure could be catastrophic. The gap between the system’s requirement for human intervention and the human’s ability to provide it creates a vulnerability that is not captured by standard performance metrics.

Furthermore, the nature of "human-in-the-loop" systems often creates a false sense of security. The term itself implies that a human is actively guiding the process, but in practice, the "loop" is often broken. The human is present in the workflow but is not mentally engaged with the task. This leads to a state of complacency where the worker serves as a rubber stamp rather than a validator. When the system is functioning properly, the human presence is a formality; when the system breaks, the human presence is a requirement for safety, but the worker is effectively unprepared to assume control.

As organizations continue to rush toward the adoption of increasingly sophisticated AI models, the focus has largely remained on the capabilities of the machines themselves. There has been far less attention paid to the human side of the equation: how to maintain, train, and challenge employees so they remain capable of operating without the crutch of automation. This involves a fundamental shift in how we think about job design. If we treat employees as mere "exception handlers" who only step in when the AI fails, we are essentially training them to be observers, not experts. We are robbing them of the daily, incremental learning that defines true professional mastery.

To mitigate this, organizations must recognize that human expertise is a strategic asset that must be actively managed and exercised, even when machines could technically do the work. This might involve periodic "manual-only" rotations, where employees perform the task without the aid of AI to ensure their skills remain sharp. It requires a cultural shift that values the human process as much as the automated output. It demands that companies build systems that don’t just ask for human intervention, but also provide the necessary context to help that human make an informed, independent judgment in the moment.

The paradox of the modern workplace is that the more we rely on AI to enhance our efficiency, the more we must double down on the importance of human judgment. The goal should not be to keep humans in the loop for the sake of appearances, but to ensure they remain capable, critical, and ready to act when the machine falls short. As Professor Anandarajan’s research suggests, the risk of losing these capabilities is real, and it is growing in the shadows of our most successful automated systems. If we fail to address this latent risk, we may find that in our quest to build smarter systems, we have inadvertently made our organizations more fragile, leaving us dependent on technologies that we no longer fully understand how to manage or override when the unexpected inevitably occurs.

The challenge ahead is to integrate AI in a way that augments human capability rather than replacing it. This requires a nuanced understanding of the delicate balance between automation and human agency. We must design workflows that keep the human mind engaged, challenging, and critical. Only by intentionally fostering this ongoing development can organizations ensure that they remain masters of their tools, rather than becoming captive to them. As we look toward the future of work, the most valuable employees will not necessarily be those who know how to use the best AI, but those who maintain the capacity to work effectively when the AI is not there to help them. This is the new, essential literacy of the digital age: the ability to maintain our own competence in a world that is increasingly trying to do the thinking for us.