Firm Valuation When AI Shapes the Business Model: A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem
How to value AI companies when their future depends on unproven breakthroughs
Standard valuation methods treat AI integration as a black box, burying crucial assumptions about whether key technical milestones will succeed. This paper proposes a structured framework that breaks down an AI company's value into specific, testable options—each with its own success probability—so investors and auditors can see exactly which bets drive the valuation. Applied to an AI energy software company, the method revealed that risk concentrates in later-stage bets, not the initial product launch.
AI company valuations swing wildly because nobody has a clear, auditable way to price them—traditional methods compress all the uncertainty into vague numbers. This framework gives investors, lenders, and acquirers a transparent way to understand what has to go right for a deal to make sense, and where the real risks actually sit. That matters most in M&A due diligence, where a buyer needs to know whether they're overpaying for speculative AI bets or betting on proven capabilities.