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Real-options model of enterprise AI adoption

Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption under Rapid Technological Progress

arXiv cs.AI@Gaurav Tewari
Paper
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Why we picked it

This academic paper provides a theoretical framework for understanding the strategic timing of AI adoption in businesses, which is relevant for any company considering implementing AI technologies.

This paper presents a two-period decision model for firms considering AI deployment.

Body

It analyzes the trade-offs between immediate deployment, a limited pilot, and waiting under uncertainty about technological progress.

The model finds that a pilot can be optimal when the expected value of organization-specific learning exceeds its cost, and that rapid progress can increase experimentation without justifying full commitment.

Original · arXiv cs.AI

Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption under Rapid Technological Progress

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.

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include ai adoption, enterprise AI, real options, decision model.

Source attribution: curated from arXiv cs.AI (@Gaurav Tewari).

#ai adoption#enterprise AI#real options#decision model#technological progress

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