AIVO Brand Alpha: A Framework for Valuing Brands in the AI Recommendation Layer

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AIVO Brand Alpha is the annual dollar difference between the AI-influenced revenue a brand captures and the revenue its market position predicts it would capture if AI assistants treated it neutrally. It measures one thing established brand valuation does not observe: how AI intermediaries allocate demand between brands at the moment of choice. AI assistants have become a new allocator of demand; Brand Alpha measures whether that allocator amplifies or suppresses a brand's existing market strength. Brand Alpha builds on two published AIVO methods. LLM Equity Valuation (LEV, WP-2026-02v2) estimates the AI-influenced revenue a brand is positioned to capture from its Organic Win Rate. Revenue at Risk (RaR) estimates revenue exposed when an assistant steers a customer who asked for the brand to a competitor. Brand Alpha sets LEV against a neutral baseline, the brand's market share, to give one signed figure: positive where the AI channel works harder for the brand than its size suggests, negative where its market position is not carried through. The paper defines the measure, states its baseline assumption, sets input standards, introduces diagnostic layers and gives a hypothetical worked example. It is a positional measure of the AI channel today, not a forecast of revenue or returns, and it complements rather than replaces existing brand valuation. Its central assumption, that AI-influenced purchases follow AI recommendation share, is stated explicitly and a validation program is set out.