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Artificial Intelligence
AI Expert Spotlight: Kate Kellogg
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We asked several MIT experts about their latest projects and what they see as the most exciting—and concerning—aspects of the AI boom.
Kate Kellogg, Professor of Work and Organization Studies
studies the implementation of narrow AI—AI systems designed to perform specific tasks—as well as generative AI, among frontline knowledge workers. She’s exploring the barriers to AI implementation and the mechanisms for addressing them.
What are you most excited about in working with AI?
I’m most excited about the tremendous gains in productivity, quality, and speed that generative AI provides to knowledge workers who use it inside the frontier of its capabilities.
What are the biggest challenges in working with AI?
In my research with a team including Fabrizio Dell’Acqua, Karim Lakhani and Edward McFowland III from Harvard Business School; Ethan Mollick from the Wharton School; and Hila Lifshitz-Assaf from Warwick Business School, we highlighted the challenge of the “jagged technological frontier” of AI’s capabilities.
Generative AI can improve workers’ performance by as much as 40 percent compared to workers who don’t use it. But when it's used outside its capabilities, worker performance drops.
A second challenge is that organizational leaders often look to junior professionals for guidance in using new technologies, but when it comes to generative AI, junior professionals are not the best teachers.
Generative AI also tends to be what we call a “skill leveler”—participants with a lower level of skill tend to benefit the most when they have access to generative AI. In our study, participants in the lower half of the skill distribution saw a 43% jump in performance compared to a 17% jump for workers in the top half of the skill distribution.
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