Current and Future Use of Large Language Models for Knowledge Work
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2025.
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Citing papers per year: 2025: 4; 2026: 6.
10 papers.
- 2026Evaluating Long-Horizon Memory for Multi-Party Collaborative Dialoguescited by 22
- 2025Glia: A Human-Inspired AI for Automated Systems Design and Optimizationcited by 16
- 2025Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Studycited by 1
- 2026Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QAcited by 1
- 2026CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Basescited by 1
- 2025BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notificationscited by 0
- 2025Impact of LLMs on Team Collaboration in Software Developmentcited by 0
- 2026Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversationscited by 0
- 2026Probabilistic Concept-Aware Steering for Trustworthy LLM Inferencecited by 0
- 2026Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agentscited by 0
- 2026Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents10 Sep 2026
- 2026CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases27 Aug 2026
- 2026Probabilistic Concept-Aware Steering for Trustworthy LLM Inference15 May 2026
- 2026Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations23 Apr 2026
- 2026Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA16 Mar 2026
- 2026Evaluating Long-Horizon Memory for Multi-Party Collaborative Dialogues1 Feb 2026
- 2025Glia: A Human-Inspired AI for Automated Systems Design and Optimization31 Oct 2025
- 2025Impact of LLMs on Team Collaboration in Software Development7 Oct 2025
- 2025BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications29 Sep 2025
- 2025Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study16 Jun 2025