Multi-case empirical validation against realised post-exit valuations

Establish whether the milestone-based real-options framework for AI-related firm valuation improves on conventional valuation methods by testing its option-level decomposition across multiple firms, Integration Depth Levels, and integration outcomes against realised post-exit valuations.

Background

The paper presents a milestone-gated real-options overlay for valuing firms whose business models are transformed by artificial intelligence. Its framework distinguishes AI Integrators from AI Providers, conditions option structures on Integration Depth Level, decomposes milestone-state value into five components, and estimates milestone probabilities using an AHP-based Success Readiness Index.

The empirical demonstration uses only one anonymised Swiss AI-native software-as-a-service firm. The authors explicitly characterise this case as evidence of protocol coherence rather than predictive or comparative validation. A broader portfolio of cases covering different integration depths, successes, failures, and disclosure conditions is therefore needed to determine whether the framework performs better than conventional DCF or comparable-multiple approaches.

References

The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.

— Firm Valuation When AI Shapes the Business Model: A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem  (2609.24181 - Kurz et al., 21 Sep 2026) in Abstract; Section 5, Discussion, final paragraphs

Whether that exposure leads to better decisions is an empirical question the framework itself cannot answer.

— Firm Valuation When AI Shapes the Business Model: A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem  (2609.24181 - Kurz et al., 21 Sep 2026) in Section 6, Conclusion