Agent-Based Data Curation Practices: Customer Responses to Human versus Algorithmic Data Requesters in Established Business-to-Business Relationships

With the increasing value generated through data curation and the rise of artificial intelligence (AI) agents that act as human agents, vendor companies in established business-to-business relationships increasingly delegate data curation tasks to algorithmic data requesters (ADRs) instead of human data requesters (HDRs). Using a randomized field experiment with a European pharmaceutical company and a follow-up online experiment, we show how customers respond to ADRs versus HDRs across two tasks: data enrichment (i.e., adding new information) and data reconciliation (i.e., updating existing records). For data enrichment, customers are more likely to agree and complete requests sent by ADRs because they expect lower effort. For data reconciliation requests, customers lean toward HDRs, reflecting stronger accuracy concerns. Interestingly, we observe only a marginal advantage in completion rates for HDRs. These findings advise vendors to match agents to data curation tasks; they should deploy ADRs for data enrichment to reduce customer burden, and they should use HDRs for data reconciliation to address error concerns. Relatedly, vendors should craft emails that match the messaging context; they should frame data enrichment messages around convenience and benefits, and they should frame data reconciliation messages around accuracy, auditability, and risk reduction.