Executive Summary: Automated equipment valuation promises to transform residual risk management by replacing subjective expert judgment with data-driven prediction. Implementation reality is more complex: data acquisition proves harder than expected, model accuracy varies dramatically by equipment category, and the human-AI workflow design determines whether the technology creates value or confusion. This article provides the practical implementation framework based on deployments at transportation and construction equipment remarketers.

A regional transportation equipment remarketer processed 3,000 returned assets annually. Their valuation process relied on three senior remarketers whose combined experience exceeded seventy years. Valuations were accurate—within 10% of realized disposition value roughly 80% of the time—but the process was slow, inconsistent across the team, and entirely dependent on institutional knowledge that would retire within a decade.