Designing AI-Mediated Communication in Teams

Abstract

AI assistants increasingly participate in work that requires employees to exchange information and coordinate decisions. We study how a firm should design this communication when workers' interests conflict with each other's and the firm's. Two workers privately know local conditions and their preferred responses, while the assistant knows aggregate project conditions. The firm discloses project information before workers report and choose their own actions. Transparency improves adaptation but can undermine truthful preference reporting. When workers agree with the firm on the productive direction, coarsening project information under weak coordination can preserve useful preference reporting at a cost in forecast precision. Under strong coordination, the firm instead prefers full project disclosure and factual reports, because preference information induces costly accommodation of private objectives. Under weak coordination, a firm-optimal policy can leave workers worse off than direct exchange; requiring their acceptance can call for further coarsening. Releasing project context after reports can sustain factual communication that early release would prevent. With separate delivery decisions that cannot be revised jointly, endogenous forwarding weakly improves firm-preferred performance over unchanged forwarding and strictly improves it in some environments. Numerical comparisons show that full initial disclosure captures most gains in the aligned environments considered, while further design has greater value when workers can favor opposing directions. The results distinguish the credibility of employees' reports from their organizational value and show how disclosure timing affects both.