The Human-in-the-Loop Experience: Navigating AI at Work

From The ABP Industry Insights Team. 

Artificial intelligence is becoming part of everyday working life. From decision support tools to automated workflows, many organisations are adopting AI systems to improve efficiency and insight. Yet people remain involved in supervising, approving, or correcting the outputs produced by AI. What is the experience for the human-in-the-loop (HITL)?  

Technology does not exist in isolation. Its success depends on how people experience and interact with it. For Business Psychologists, understanding the human-in-the-loop experience can help organisations adopt AI in ways that are both effective and healthy for those who use it. 

The Promise of Human-AI Collaboration 

Research on human-AI interaction suggests that collaboration between people and intelligent systems can bring significant benefits. AI can analyse large datasets quickly, identify patterns, and automate repetitive tasks. In practice, this can mean that professionals spend less time on routine work and more time on judgement, creativity, and problem-solving. 

For example, AI tools may help analysts process complex information faster, or support managers by summarising large volumes of feedback or performance information. When used well, AI can reduce cognitive load, support better decision-making, and allow people to focus on the more human aspects of their work. 

These positive outcomes help explain why organisations are increasingly investing in AI-supported systems. 

The More Complex Side of the Experience 

However, research also shows that the psychological experience of working with AI can be mixed. Alongside the benefits, some individuals report anxiety, uncertainty, or reduced confidence in their own judgement when interacting with automated systems. 

While AI can provide valuable insights, over-reliance can weaken critical thinking or create uncertainty about when to challenge the technology. In some cases, people may defer too quickly to algorithmic recommendations, a tendency sometimes described as algorithmic authority. Related research discusses the idea of cognitive surrender, where individuals accept AI outputs with limited scrutiny.  

These experiences may influence how quickly people trust or adopt AI in their work. If individuals feel confident and supported, adoption may accelerate. If they feel uncertain or exposed, progress may slow. 

The Supervisory Challenge 

These tensions are particularly visible in roles where people are responsible for monitoring or supervising AI systems. 

In many organisations, AI outputs must be reviewed or approved by a human before they are used. This is often a healthy design feature. It allows people to correct errors, apply contextual judgement, and ensure ethical oversight. 

In addition, regulation increasingly requires human involvement. For example, policy frameworks such as the EU AI Act emphasise the need for meaningful human oversight in high-risk AI systems. 

Yet these responsibilities can also create pressure. When a human must approve or override AI outputs, questions arise about who ultimately holds responsibility. If an AI-supported decision causes harm, the human supervisor may still be held accountable. 

Any lack of clarity can leave individuals feeling exposed. They may wonder whether they are expected to trust the technology or challenge it, and what happens if they make the wrong call. 

The Vigilance Paradox 

Another challenge identified in research on human-automation systems is sometimes described as the vigilance paradox.