LLM Equity Valuation (LEV™): A Framework for Quantifying AI Purchase Recommendation Equity in Brand Acquisitions

This paper introduces LLM Equity Valuation (LEV™), a methodology for measuring a brand's standing in AI purchase recommendation systems and comparing it with the brand's standing in its market. As large language models become a significant intermediary in consumer purchase decisions, established brand valuation frameworks, built on financial performance and human stakeholder perception, do not observe how AI intermediaries allocate demand. The core input is the Organic Win Rate: the share of unprompted category purchase conversations in which an AI model makes the brand its final recommendation. It decomposes into Inclusion Rate (visibility) and conditional win rate (conversion). The headline measure is the AI Share Index, the brand's share of AI recommendations relative to its market share, which is dimensionless and comparable across categories and transactions. LEV converts this position into an estimate of annual AI-influenced revenue, and an AI Revenue Gap can be capitalised at the deal multiple for comparison with transaction value. The adversarial CODA probe (WP-2026-01) is retained as a diagnostic. This revision corrects the April 2026 version, whose formula overstated AI-reachable revenue and whose ratios mixed revenue with enterprise value. The Grüns / Unilever case study is restated and its dollar figures withdrawn pending an organic re-probe. The framework's central assumption, that AI-influenced purchases follow AI recommendation share, is stated explicitly, and a validation programme is set out.