Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption under Rapid Technological Progress
A research paper published on arXiv (cs.AI) on 2026-09-14 presents a two-period decision model for enterprise AI adoption under uncertainty. The model considers three choices: immediate deployment, a limited pilot, or waiting. Key findings include: a mean-preserving increase in frontier uncertainty raises the value of waiting and piloting but leaves immediate deployment unchanged when its payoff is affine in the frontier; faster expected frontier progress can reduce the relative attractiveness of immediate deployment when deployed architecture captures only a limited share of future improvement; and a pilot dominates waiting exactly when the expected value of learning exceeds the cost of the pilot.
The paper develops a real-options framework for enterprise AI adoption, addressing the timing problem created by rapid technological progress and irreversible implementation. It models a firm's choice among immediate deployment, a limited pilot, and waiting, and derives conditions under which each strategy is optimal. The model highlights the trade-off between current operating value and the option value of waiting or learning through piloting.
The model formalizes the value of organizational learning through pilots as a real option. It shows that piloting is optimal when the expected value of learning exceeds the pilot's cost, and that uncertainty about the technology frontier increases the value of both waiting and piloting. The affine payoff assumption for immediate deployment under uncertainty is a key technical condition.
For enterprise AI adopters, the paper suggests that under rapid technological progress, committing early to a specific architecture may be suboptimal if the architecture cannot capture future improvements. Piloting allows firms to build capabilities without full commitment, balancing learning and flexibility.
The framework provides a decision-making tool for enterprises to evaluate AI adoption timing, potentially reducing the risk of investing in soon-to-be-obsolete technology and optimizing the allocation of resources between immediate deployment, piloting, and waiting.
The model implies that as AI technology continues to advance rapidly, enterprises may increasingly favor pilot programs over full-scale deployment until the technology frontier stabilizes or the firm's architecture can incorporate future improvements. Future research could extend the model to multi-period settings or incorporate competitive dynamics.