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Generative AI and collaboration: opportunities for cultivating collective intelligence
Abstract
The transformative potential of artificial intelligence (AI) is reshaping collaboration within organizations, evoking both excitement and concern. As an individual production technology, AI risks fragmenting workflows, isolating workers, and undermining the human-centered collaboration vital for creativity and innovation. However, as a coordination technology, AI holds enormous promise for enhancing collective intelligence. By considering the fundamental processes underlying intelligence in any system—including reasoning, memory, and attention—we can envision ways AI can overcome traditional barriers to effective collaboration to elevate collective intelligence. This paper explores how AI can help clarify team goals and resolve conflicting motives to enhance collective reasoning; provide personalized knowledge assistance, connect complementary expertise, and mitigate biases to augment collective memory; and facilitate attention to priorities and manage asynchronous and synchronous coordination to optimize the use of collective attention. Despite these opportunities, integrating AI into collaboration presents challenges, including potential impacts on trust, cohesion, and ethical concerns. The paper outlines a research agenda to address these challenges and explore AI’s role in fostering inclusive, efficient, and innovative collaboration. By leveraging generative AI responsibly, organizations can amplify human abilities, creating synergistic outcomes that redefine the future of work.
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“Moreover, collaboration often depends on human alignment ( Ch7 ) including shared understanding, and social negotiation, which are difficult to automate through GenAI alone [42].”
From The Fast and Spurious: Developer Productivity with GenAI (2025) · cites this paper as [42]
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“…humans inter-acting with artifacts and AI, the same three processes are distributed across people and tools—attention as coordination and cueing, memory in external representations, and reasoning in shared problem-solving procedures (Clark & Chalmers 1998; Hollnagel & Woods 2005; Hutchins 1995).”
How many memory systems are there?
Tulving, E. (1985) · American Psychologist · doi:10.1037/0003-066x.40.4.385 · Cited by 1,909
The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour
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“For management scholars, this fear highlights a need to examine how overreliance on AI-based production systems impacts individual memory and learning (Sparrow et al. 2011), group dynamics, worker well-being, and long-term organizational resilience (Bankins et al. 2024; Schmutz et al. 2024).”
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Cited by
The Fast and Spurious: Developer Productivity with GenAI
Afroz, S., Feng, Z., Menezes, T., Kimura, K., Trinkenreich, B., Steinmacher, I., & Sarma, A. (2025) · arXiv · doi:10.48550/arxiv.2510.24265 · Cited by 11
Metadata
- Authors
- Woolley, A. W.
- Venue
- Journal of Organization Design
- Published
- 2025-10-10
- Volume
- 15
- Issue
- 1
- Pages
- 45-50
- ISSN
- 2245-408X
- Cited by
- 13
- References
- 8
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Carnegie Mellon University
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