The Human Side of Artificial Intelligence and the Relevance of Human Factors

Much of the conversation surrounding Artificial Intelligence (AI) centers on its promise to take humans out of the loop in order to reduce errors, streamline decision-making processes, and ultimately make complex systems more efficient. That framing can make the role of human factors engineering seem less pertinent as AI becomes more prevalent.

In reality, the opposite is true. As AI becomes more capable, systems become more complex—and as complexity increases, understanding how people interact with those systems becomes not less important—but essential.

How Automation Reshapes Human Roles

History can offer a clear lens: automation has always shifted the nature of human work rather than removing it entirely. This pattern is not new: automation has historically taken over certain manual tasks in aviation, healthcare, and other high-stake domains. In the same vein, automation has also introduced entirely new cognitive demands including monitoring and supervising that automation, and even diagnosing problems and intervening when necessary.

AI follows this same pattern, albeit faster, and at a greater scale. AI systems are adaptive, probabilistic, and dynamic. AI system behavior is inherently less predictable and less transparent. As a result, the role of the human operator changes from direct control to one of supervision, judgment, and system recovery when conditions deviate from the norm.

With AI, the human is still accountable—but often with less direct visibility into what the system is doing and why (this is partially why explainable AI is so important, especially in a human-automation relationship). That shift is precisely where human factors engineering becomes critical.

Capability Comes with Complexity

AI is often discussed in terms of performance gains: improved accuracy, faster processing, better optimization. What is discussed less frequently is the complexity AI introduces at the system level.

AI-driven systems tend to be:

• More opaque, making it harder for users to understand system reasoning

• More dynamic, changing behavior based on data, context, or learning

• More scalable, allowing small issues to propagate rapidly across operations

These characteristics don’t just create technical challenges, they create cognitive ones: interpreting confusing system behavior, allocating the right amount of trust in automation, recognizing when performance is degrading in time, and intervening effectively, all often under time pressure. If these cognitive demands are not taken into account, cognitive workload will increase and failures will be harder to detect and recover from.

Automation Can Accelerate Mistakes

One of the greatest risks of AI-enabled systems is not that they fail—but that they fail quietly, or in ways that humans are poorly equipped to detect.

Without careful attention to human factors, automation can cause unforeseen issues: operators may place too much trust in the system, for example, and disengage from active monitoring. Interfaces may hide important information about system state, leading to downstream errors and failures. When these errors and failures occur, humans are expected to jump in cold with little visibility as to what caused the problem. 

In these cases, AI does not eliminate error; rather AI enables, accelerates, and amplifies it.

In AI-enabled systems, even a small design flaw such as a faulty assumption, a poorly considered interaction, or a misunderstood system behavior can propagate rapidly across users, platforms, and operational contexts. Human factors mitigates this domino effect early through  focusing on how people actually interact with automated systems, not how we assume they will—and by designing those systems accordingly.

Human Factors as Risk Management

Human factors can be misunderstood as a compliance exercise or a usability check that slows development. In reality, human factors plays a far more strategic role, particularly (or especially) in AI-enabled systems.

Effective human factors engineering:

• Aligns system behavior with human cognitive and physical capabilities

• Ensures performance holds up when conditions aren’t ideal, i.e., during high workload, unexpected events, and the edge cases that matter most in real operations

• Encourages appropriate trust in automation

• Enables systems to scale safely by accounting for real-world human interaction

In other words, human factors is not about limiting what AI can do. Human factors is about ensuring that what AI does actually works in practice.

Organizations that treat human factors as foundational, rather than as an afterthought, are better positioned to deploy AI responsibly, effectively, and at scale.

Why AI Cannot Replace Human Factors

AI excels at identifying patterns and optimizing outcomes based on data. Human factors engineering addresses a different class of questions entirely.

Human factors asks:

• How do people understand and respond to system behavior?

• What happens when conditions deviate from the norm?

• Where does responsibility lie when automation fails?

• How do humans detect, diagnose, and recover from problems?

These are not questions AI can answer on its own. These questions require system-level thinking, empirical evaluation, and an understanding of human behavior in context.

As AI becomes more integrated into decision-making and control, these questions become more, not less, important.

The Future of AI is Still Human

While AI represents a significant technological shift, it does not change a fundamental truth: complex systems succeed or fail based on how well people and technology work together.

Human factors engineering provides the tools to understand that relationship, to anticipate risks, design for resilience, and ensure that increased automation leads to better outcomes rather than new vulnerabilities.

In the age of AI, human factors is not optional. Rather, human factors remains the discipline that enables AI-enabled systems to perform safely, effectively, and sustainably in the real world.