AI Agents Are About to Flood the Workforce. No One’s Ready for It
Your next coworker might well be an AI agent—and will require a whole new model of workplace interactions.
The emergence of AI agents—autonomous systems powered by large language models—has led to a surge in companies offering digital employees. These agents, marketed as perfect work companions, are available 24/7, display superhuman intelligence, and never complain. However, little attention has been paid to how these AI coworkers behave in real work settings.
Julie Bedard, a Boston Consulting Group partner, observed the ambiguity around how people describe AI at work three years ago, as ChatGPT's rapid adoption convinced executives of its productivity potential. She wondered if these AI entities were merely tools, colleagues, or something in between. In a recent survey of 1,261 managers, 22 percent reported adding AI agents to their organizational charts.
Rather than being human, these agents possess traits that encourage employees to anthropomorphize them, making them seem more like coworkers. For instance, Anything, a startup that launched AI coworkers in August, personifies its agents with names, roles, and avatars designed to resemble humans. The agents are integrated into workplace communication platforms like Slack and email, evolving to match employees' communication styles.
While AI agents offer benefits such as constant availability and a lack of office politics, they also pose risks. When work is attributed to an AI agent instead of a human employee, managers are 18 percent less likely to detect errors. Moreover, forming emotional connections with AI can lead to misapplication of workplace norms.
Some tech companies, like Lattice, intentionally avoid anthropomorphizing AI to maintain accountability. The success of AI agents hinges on their ability to work seamlessly alongside human employees, a challenge that companies are only beginning to navigate.
Written by urgent.news from Wired Business's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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