Work Experience’s Surprising Impact on AI

A recent study from the University of Rochester sheds light on how different types of work experience affect how employees work with AI. The research looks at two main kinds of work experience: specific task knowledge and seniority, and how they impact the way humans and AI work together.

“We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers,” the researchers explain. “We were surprised by what we found in the study. The different dimensions of work experience have distinct interactions with AI and play unique roles in human-AI teaming.”

Different interactions

“While one might think that less experienced workers should benefit more from the help of AI, we find the opposite — AI benefits workers with greater task-based experience. At the same time, senior workers, despite their greater experience, gain less from AI than their junior colleagues,” the authors continue.

Further exploration uncovers that the somewhat limited boost in productivity from AI isn’t due to seniority itself, but rather it’s because more experienced employees tend to be more sensitive to AI’s shortcomings, which erodes their trust in AI.

“This finding presents a dilemma: Employees with greater experience are in a better position to leverage AI for productivity, but the senior employees who assume greater responsibilities and care about the organization tend to shy away from AI because they see the risks of relying on AI’s assistance. As a result, they are not effectively leveraging AI,” the authors conclude.

The researchers recommend that employers take a thoughtful approach when introducing AI into the workplace, taking into account the varying levels and types of employee experience. Newer employees with less task-specific knowledge may face challenges in making the most of AI.

On the other hand, more senior employees with extensive organizational experience might have concerns about the potential risks associated with AI. Effectively addressing these distinct challenges is crucial for achieving successful collaboration between humans and AI.

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