The Impact of Generative AI Adoption on Organizational Networks: Evidence From A Field Experiment

While generative AI (GenAI) tools can boost individual productivity, their effects on intra-organizational networks for collaboration and knowledge sharing remain largely unknown. The authors theorize that adopting GenAI reshapes employees’ interaction patterns by acting as a translator (making collaboration easier) and as a knowledge catalyst (increasing the value of individuals as sources of knowledge), thereby rewiring interactions. The authors further propose role-based differences in these effects across specialists (deep experts) and generalists (broad integrators). They test these ideas in a randomized field experiment with 316 employees at a European technology services firm, randomly assigning 42 teams to either use a GenAI assistant customized with organization specific knowledge (treatment) or continue as usual without it (control). After three months, employees with the GenAI tool became significantly more central in their collaboration and knowledge-sharing networks than the control group, reflecting expanded ties and more frequent knowledge exchange. Specialists (technical support personnel) experienced greater increases in knowledge centrality than generalists (sales staff), while generalists gained the most in terms of performance, measured as number of projects executed. These findings provide causal evidence that GenAI adoption can rewire organizational social structure and augment performance differentially.