AI Win-Rate℠

In sales, win rate is opportunities won divided by opportunities available. It is one of the few commercial measures that survives contact with a board. In AI-mediated buying, there has been no equivalent, not because the outcome is unimportant, but because there is no logged opportunity pipeline from which it can be calculated. Decisions happen inside conversations. Losses are largely invisible. And the conventional visibility metrics measure presence, not who actually wins the decision.

AIVO Meridian has now published a formal methodology for measuring that outcome. "AI Win-Rate: A Board Metric for AI-Mediated Buying (Methodology v1.0)," by Paul Sheals and Tim de Rosen, is out now under DOI 10.5281/zenodo.22280231.

AI Win-Rate measures the proportion of defined decision conversations in which a brand receives the assistant's decisive recommendation. Put plainly, who wins the decision when the conversation reaches a recommendation.

The difficult part is not the formula. It is defining the measurement universe correctly, and the methodology sets out five conditions a valid measurement must satisfy. Among them: the opportunity set, which must be declared before measurement begins and cannot subsequently contract. The definition of a win, which can count even when the assistant reaches a recommendation without being explicitly asked to name a provider. The outcome space, which must distinguish among five possible outcomes, including a result that conventional appearance-based metrics cannot express: the assistant decisively directing the buyer to an intermediary rather than recommending any provider. And the loss mix, which must accompany any published win rate, along with the conditions under which the methodology requires that no win-rate figure be reported at all.

The distinction matters because a brand can be highly visible to AI systems and still lose the decision. It can be cited but not chosen. It can appear in the conversation and disappear from the recommendation. AI Win-Rate measures the outcome, not the appearance.

AI Win-Rate: A Board Metric for AI-Mediated Buying (Methodology v1.0)

In sales, win rate is opportunities won divided by opportunities available. It is one of the few commercial measures that survives contact with a board. In AI-mediated buying it has not been measurable, because the pipeline that produces it does not exist: nothing is logged, losses are invisible, and the outcomes have no equivalent in a sales funnel.This document defines AI Win-Rate℠ — the proportion of decision conversations in which an AI assistant recommends a brand, product or service outright. Put plainly: who wins the final recommendation in a multi-turn conversation.It specifies what counts as an opportunity and why the set must be declared before measurement and never contract; what counts as a win, including a recommendation the assistant arrives at without being asked; the five-outcome space a measurement must be able to record, including the case that appearance-based measures cannot express — an assistant answering decisively by directing the buyer to an intermediary rather than to a provider; the loss mix that must travel with any published figure; and the conditions under which a figure may not honestly be reported at all.It is a definition of a metric, not of an instrument. Sales win rate is not defined by the CRM that reports it. Any measurement design meeting the five conditions set out in §9 can produce an AI Win-Rate; the instruments AIVO Meridian applies are described there as examples rather than as part of the definition.This is the first formal specification of the metric. It formalises measurement work developed alongside Agentic Brand Control, the category published by AIVO in May 2026 (10.5281/zenodo.20154028) and extended in August 2026 (10.5281/zenodo.21998000). Agentic Brand Control names the discipline; AI Win-Rate is the measure it is managed against.The document contains no measurement results; it is a specification of method. AI Win-Rate℠ is a service mark of AIVO Meridian.

ZenodoSheals, Paul and de Rosen, Tim

AIVO Meridian