Human-AI Collaboration in Corporate Valuation: Experimental Evidence with a Valuation AI Agent

Abstract

In an era where AI can deliver increasingly sophisticated hard information, we study how AI can facilitate human soft information production and make human-AI collaboration in corporate valuation more productive. We develop a retrieval-augmented AI agent that reads financial filings and produces interactive dashboards and valuation analytics. We embed an experiment in an advanced business course in which students use the AI agent to value real firms, varying the amount of AI-supplied hard information across three interfaces: bare retrieval (Low-Hard), dashboards that summarize financials but do not propose a valuation (Medium-Hard), and dashboards plus AI-generated valuations (High-Hard). Using full chat logs and valuation memos from participants, we construct rich measures of soft information and relate them to ex ante financial statement analysis (FSA) knowledge. We find a non-monotonic effect of AI-supplied hard information: relative to Low-Hard, dashboards in Medium-Hard substantially increase soft information production, whereas adding AI valuations in High-Hard yields much smaller incremental gains and appears to induce anchoring. FSA knowledge amplifies the benefits of dashboards and mitigates, but does not eliminate, crowding out in High-Hard. Our results clarify when AI acts as a complementary hard-information engine versus a substitute for human soft information production, and offer guidance for the design of valuation tools and curricula in the LLM era.