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
From the original article
1 min read at arxiv.orgArtificial 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.
By Gaurav Tewari · arxiv.org
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