Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption under Rapid Technological Progress
Real-options model shows firms should pilot AI early and commit late because rapid frontier progress raises experimentation value over irreversible deployment.
The paper builds a two-period decision model where firms choose among immediate AI deployment, a limited pilot, or waiting under frontier uncertainty. It derives five timing results plus a comparative result on where learning occurs, including that frontier uncertainty raises the value of waiting and piloting. Valuable organization-specific learning creates a region where 'pilot early, commit late' is optimal, and a closed-form modularity threshold exists above which immediate deployment dominates. A continuous-time extension recovers the standard result that uncertainty raises adoption thresholds while capability and modularity lower them.
- Two-period model compares immediate deployment, pilot, and waiting under frontier uncertainty
- Greater frontier uncertainty raises value of waiting and piloting, not immediate deployment
- Closed-form modularity threshold above which immediate deployment dominates
- Continuous-time extension: uncertainty raises adoption threshold; capability and modularity lower it
Full article255 words · extracted from arxiv.org · click to collapse
Artificial intelligence presents firms with an unusual timing problem. The technology frontier is improving rapidly, implementation is partly irreversible, and organization-specific capabilities are accumulated through action. This paper develops a two-period decision model of AI deployment under uncertainty in which a firm chooses among immediate deployment, a limited pilot, and waiting. Deployment earns current operating value but exposes the firm to architectural obsolescence; waiting preserves the option to adopt after the frontier is observed; a pilot sacrifices current operating value to build organization-specific learning without full commitment. The model yields five central timing results and a sixth comparative result on where learning occurs. First, 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. Second, faster expected frontier progress can reduce the relative attractiveness of immediate deployment when deployed architecture captures only a limited share of future improvement. Third, a pilot dominates waiting exactly when the expected value of the capability it builds exceeds its cost. Fourth, sufficiently valuable organization-specific learning creates a nonempty region in which "pilot early, commit late" is optimal. Fifth, there is a closed-form modularity threshold above which immediate deployment dominates the best outside option. Sixth, production learning and pilot-specific learning affect the timing margin differently. A continuous-time extension recovers the standard result that uncertainty raises the adoption threshold while capability and modularity lower it. The paper separates deploying, experimenting, and waiting, and shows why rapid progress can rationally increase experimentation without justifying irreversible commitment.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.15919