Generative AI changes what people and organizations remember and learn
When AI performs, what remains? An integrative review of generative AI and cognitive capital in individuals and organizations
Human-Computer InteractionArtificial Intelligence
Summary
Generative AI is helping people work faster, but it might also change what skills and knowledge they keep. The authors studied how AI affects what people and organizations know and how they learn over time. They explain that using AI can sometimes replace practice that builds skills, sometimes support learning, and can affect how groups share diverse knowledge. The paper suggests ways organizations can carefully design AI use to keep human thinking strong alongside AI help.
What this means in practice
- •For human resources teams: Develop guidelines for AI use that preserve employee skills and support long-term learning within organizations.
- •For knowledge management teams: Design AI tools that balance performance gains with maintaining organizational knowledge diversity.
A survey. It maps existing work.
Authors
Giuseppe Riva, Stefania La Rocca, Brenda K. Wiederhold, Andrea Gaggioli
Abstract
Generative artificial intelligence is rapidly entering knowledge work, and its short-term productivity benefits are increasingly documented. Less is known about what happens, over time, to the knowledge, skills and judgment that remain in people and organizations. This integrative narrative review addresses that question through the construct of cognitive capital: the accumulated, renewable and depletable stock of knowledge, cognitive and metacognitive capabilities, and practices that enables an individual or an organization to learn, interpret information, exercise judgment, solve problems and adapt. We distinguish individual and organizational cognitive capital from the capability of the human-AI system, so that gains in joint performance and losses in human capability can be observed at the same time. Drawing on experimental, field and conceptual work, we propose four mechanisms through which interaction with AI affects cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation. Seven principles summarize the current evidence, including that assisted performance differs from retained competence, that the same technology can support or displace learning depending on design and use, and that individual gains can coexist with reduced collective diversity. For each mechanism we derive testable propositions, and we discuss five design levers for organizations as proposals to be validated rather than established practice. We conclude that cognitive capital offers a multilevel frame for governing AI adoption and outline a research agenda centered on longitudinal, multilevel measurement.