Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption (arXiv 2609.15919)

Forum topic · 小凯 · 2026-09-16

Summary

This paper by Gaurav Tewari (arXiv:2609.15919) develops a two-period decision model of enterprise AI deployment under uncertainty, in which a firm chooses among immediate deployment, a limited pilot, and waiting. Deployment earns current operating value but risks architectural obsolescence; waiting preserves the option to adopt after the technology frontier is observed; piloting sacrifices operating value to build organization-specific learning without full commitment. The model yields five central timing results plus a comparative result on where learning occurs: mean-preserving increases in frontier uncertainty raise the value of waiting and piloting but not immediate deployment when payoffs are affine in the frontier; faster expected frontier progress can reduce deployment's relative appeal when architectures capture only a limited share of future improvements; piloting strictly dominates waiting when learning value exceeds its cost; sufficiently valuable learning creates a non-empty 'pilot early, commit late' optimal region; and a closed-form modularity threshold determines when deployment dominates the best external option. A continuous-time extension recovers standard real-options results. The framework distinguishes deployment, experimentation, and waiting, explaining why rapid AI progress can justify experimentation without warranting irreversible commitment.

Paper Overview

Field: Machine Learning
Author: Gaurav Tewari
Posted: 2026-09-14
arXiv: 2609.15919

Abstract (translated summary)

Artificial intelligence presents firms with an unusual timing problem: the technology frontier is improving rapidly, implementation is partly irreversible, and organization-specific capabilities accumulate through action. The 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.

Immediate deployment earns current operating value but exposes the firm to architectural obsolescence.
Waiting preserves the option to adopt after the frontier is observed.
Piloting sacrifices current operating value to build organization-specific learning without full commitment.

Key Results

The model yields five central timing results and a sixth comparative result on where learning occurs:

1. A mean-preserving increase in frontier uncertainty raises the value of waiting and piloting, but has no effect on immediate deployment when its payoff is affine in the frontier. 2. When the deployed architecture captures only a limited share of future improvements, faster expected frontier progress can reduce the relative appeal of immediate deployment. 3. When the expected value of pilot-built capabilities exceeds its cost, piloting strictly dominates waiting. 4. Sufficiently valuable organization-specific learning creates a non-empty region where the "pilot early, commit late" strategy is optimal. 5. There exists a closed-form modularity threshold above which immediate deployment dominates the best external option. 6. Production learning and pilot-specific learning affect timing margins differently.

A continuous-time extension recovers the standard real-options results: uncertainty raises the adoption threshold, while capabilities and modularity lower it.

Takeaway

The paper distinguishes deployment, experimentation, and waiting as distinct strategic actions, and shows why rapid AI progress can rationally increase experimentation without justifying irreversible commitment.

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