Description

This year’s edition of the Futre of Work report, the third in the series, centers on research related to integrating LLMs into work. You will find content related to:

• LLMs for Information Work: How do LLMs affect the speed and quality of common information work tasks? LLMs can boost productivity for information workers, but they also require careful evaluation and adaptation.
• LLMs for Critical Thinking: How can LLMs help us break down and build up complex tasks? LLMs can help us tackle complex tasks by provoking critical thinking, enabling microproductivity, and shifting the balance of skills.
• Human-AI Collaboration: How can we collaborate effectively with LLMs? Effective collaboration with LLMs depends on how we prompt, complement, rely on, and audit them.
• LLMs for Complex and Creative Tasks: How can LLMs tackle tasks that go beyond simple information retrieval or generation? LLMs can support complex and creative tasks by, for instance, enhancing metacognition.
• Domain-Specific Applications of LLMs: How are LLMs being used and affecting different domains of work? We focus specifically on software engineering, medicine, social science, and education.
• LLMs for Team Collaboration and Communication: How can LLMs help teams work and communicate better? LLMs can help teams improve interaction, coordination, and workflows by providing real-time, retrospective feedback and leveraging holistic frameworks.
• Knowledge Management and Organizational Changes: How is AI changing the nature and distribution of knowledge in organizations? LLMs might, for instance, finally eliminate knowledge silos in large companies.
• Implications for Future Work and Society: What implications will AI have for the future of work and society? We can shape AI’s impact by addressing adoption disparities, fostering innovation, leading like scientists, and remembering that the future of work is in our control.