When 5,000+ support agents got AI help, the least experienced gained most, so does AI's payoff depend on who's doing the work?

This explores whether AI helps every worker equally or whether the payoff depends on the person. The corpus says it clearly depends on the person, though not in the way you might expect. The best-known evidence comes from a study of more than 5,000 customer support agents. AI assistance raised issues resolved per hour by about 15% on average, but the gains went mostly to the least experienced agents, who got faster and better at the same time. The most experienced agents got slightly faster, and the quality of their work dropped a little Does AI assistance help less experienced workers most?. A field experiment at Procter & Gamble points the same way. Individuals using AI produced solutions as strong as two-person teams working without it, and AI pushed people from different professional backgrounds toward similar, more balanced proposals Can generative AI replace the benefits of having a human teammate?. Both studies suggest AI works partly as a leveler: it raises the floor more than the ceiling.

The catch is that this leveling may be borrowed rather than earned. When workers used AI on tasks inside their existing skills, productivity went up. When they used it to learn something new, the gains disappeared and the learning itself suffered When does AI actually boost worker productivity?. A separate study found that people who did much better with generative AI showed no improvement when they later did similar tasks on their own Does AI assistance help workers learn lasting skills?. Put these next to the support-agent result and an awkward possibility appears. The novices who gain the most output may be the people least likely to come away more skilled. So whether a worker "benefits" depends on whether you measure today's output or the expertise they'll have next year.

The individual also shapes the benefit through how they see their own abilities. The corpus describes an "LLM Fallacy": people credit their own competence for what the AI actually produced. This is a different problem from hallucination or over-trusting the tool, and it can happen even when the AI's output is correct How does AI-assisted work reshape how people see their own abilities?. Two workers with the same AI results can therefore come away with very different, and sometimes inflated, beliefs about what they can do alone.

The individual may matter less than it seems, though. A study of junior and senior engineers found that company rules set the boundaries before personal preference comes into play: tool mandates, approved-tool lists, and data policies. Within those limits, novices swung between leaning on the tool too much and avoiding it Does personal preference shape how engineers use AI tools?. Exposure also isn't spread evenly. In female-dominated occupations it reaches all skill levels, including lower-paid workers with fewer resources to adapt Does AI exposure hit low-wage workers harder in some fields?. Workers' own preferences add one more variable. In 45% of occupations, the level of AI collaboration workers most want is equal partnership, yet much of the investment is going elsewhere What collaboration level do workers actually want with AI?. Researchers studying "thought partners" argue that real partnership needs mutual understanding and a shared picture of the problem What makes an AI a true thought partner, not just a tool?. That suggests how much someone gains may depend less on their talent than on whether the tool can model what they in particular know and need.