Current and Future Use of Large Language Models for Knowledge Work

2025-03-21

AI Summary

This study investigates the evolving adoption and human-AI collaboration mechanisms of large language models (LLMs) in knowledge work. Addressing the lack of longitudinal empirical evidence, we conducted two waves of longitudinal surveys (N=216, N=107) complemented by mixed methods—including quantitative analysis, temporal comparison, topic modeling, and qualitative coding—to systematically track LLM usage over one year in authentic workplace settings. We propose a three-dimensional adoption framework—task-level, process-level, and data-level—that captures the critical transition from isolated tool use to deep workflow integration and private data fusion. Results identify high-frequency applications (e.g., code generation, text refinement) and persistent bottlenecks (e.g., data privacy, output controllability), while empirically confirming knowledge workers’ strong preference for embedding LLMs into local systems and core business processes. The study contributes both a theoretical framework and actionable insights for designing effective, context-aware AI-augmented work systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in natural language. LLMs have the potential to increase productivity and reduce tedious tasks in an unprecedented way. A systematic study of LLM adoption for work can provide insight into how LLMs can best support these workers. To explore knowledge workers' current and desired usage of LLMs, we ran a survey (n=216). Workers described tasks they already used LLMs for, like generating code or improving text, but imagined a future with LLMs integrated into their workflows and data. We ran a second survey (n=107) a year later that validated our initial findings and provides insight into up-to-date LLM use by knowledge workers. We discuss implications for adoption and design of generative AI technologies for knowledge work.

Problem

• Study current and future LLM use in knowledge work
• Explore how LLMs enhance productivity and reduce tasks
• Survey workers on LLM adoption and desired integration

Innovation

• Surveying knowledge workers on LLM usage
• Exploring LLM integration into workflows
• Validating findings with follow-up surveys