Capable but Not Deployable: Institutional Constraints on AI Exposure in Finance

Abstract

Empirical measures of AI exposure ask language models to score O*NET tasks for technical feasibility. In finance, technically feasible tasks must still pass through review, documentation, supervision, confidentiality controls, and accountable human sign-off before entering production. We measure the gap between feasibility and institutional deployability using 2,199 O*NET tasks across 99 finance-and-insurance occupations. We score each task with eight frontier models and a prompt ladder that moves from bare capability to finance-industry context and named regulatory regimes. The within-model institutional markdown is about one-fifth of the mean feasibility score, and positive for all eight models. The markdown is largest for regulated, client-facing credit and advice roles and smallest for marketing, software, and support roles. Cross-model agreement also declines as finance context is added: models agree more about what AI can do than about what financial institutions can deploy. Mapping exposure to publicly traded firms through pre-ChatGPT staffing shares, we find that the pricing content resides in the institutional layer: firms in the top half of the markdown distribution underperform the bottom half by roughly 25 percentage points in market-adjusted cumulative abnormal returns over the three years after ChatGPT, while sorting on technical exposure alone produces no gap. The differential lies outside the range the same design produces over every pre-ChatGPT window of equal length, though with one event window and few subsector clusters we read it as evidence on where return information resides rather than as a causal estimate. Especially in regulated industries, deployable exposure rather than technical feasibility is the more relevant measure of AI exposure.