AI Win-Rate: A Board Metric for AI-Mediated Buying (Methodology v2.0)

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. New in version 2.0. Version 1.0 recorded that the width at which a figure ceases to be a rate and becomes a range was a convention requiring a stated basis. This version supplies that basis as a procedure any instrument applies to itself: the three steps by which an instrument establishes its own precision floor, and a reporting threshold — published as a rate where the 95% interval is no wider than ±10 points, as a range between ±10 and ±20, and withheld beyond ±20 — expressed in decision conversations rather than prompts, queries or API calls. Also new: a paired requirement for before-and-after claims, which detects a genuine ten-point improvement four times in five where an unpaired comparison misses it in ninety-three cases out of a hundred; a requirement that a scenario set declare the basis on which it was constructed, since fixing a set in advance does not on its own establish that it represents anything; the separation of measurement from commercial modelling; how figures from different assistants combine; and the statement that replication is not repetition over time, longitudinal smoothing being unable to distinguish model drift from inference noise. The win rate is terminal-only. Earlier-turn leadership and displacement travel beside it as declared companions, never as components of it. A first answer characteristically does not resolve to a single entity and therefore contains no win to count; any figure derived from position in an opening list is a prediction of a win rate calibrated against one, and is not a win rate. Supersedes version 1.0 (DOI 10.5281/zenodo.22280231).

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Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-08
DOI
https://doi.org/10.5281/zenodo.22663407
Primary Topic
AI in Service Interactions
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article
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article

AI Win-Rate: A Board Metric for AI-Mediated Buying (Methodology v2.0)

Tim de Rosen, Paul Sheals
Zenodo (CERN European Organization for Nuclear Research)
AI in Service Interactions
article

AI Win-Rate: A Board Metric for AI-Mediated Buying (Methodology v2.0)

Tim de Rosen, Paul Sheals
article en

Abstract

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. New in version 2.0. Version 1.0 recorded that the width at which a figure ceases to be a rate and becomes a range was a convention requiring a stated basis. This version supplies that basis as a procedure any instrument applies to itself: the three steps by which an instrument establishes its own precision floor, and a reporting threshold — published as a rate where the 95% interval is no wider than ±10 points, as a range between ±10 and ±20, and withheld beyond ±20 — expressed in decision conversations rather than prompts, queries or API calls. Also new: a paired requirement for before-and-after claims, which detects a genuine ten-point improvement four times in five where an unpaired comparison misses it in ninety-three cases out of a hundred; a requirement that a scenario set declare the basis on which it was constructed, since fixing a set in advance does not on its own establish that it represents anything; the separation of measurement from commercial modelling; how figures from different assistants combine; and the statement that replication is not repetition over time, longitudinal smoothing being unable to distinguish model drift from inference noise. The win rate is terminal-only. Earlier-turn leadership and displacement travel beside it as declared companions, never as components of it. A first answer characteristically does not resolve to a single entity and therefore contains no win to count; any figure derived from position in an opening list is a prediction of a win rate calibrated against one, and is not a win rate. Supersedes version 1.0 (DOI 10.5281/zenodo.22280231).

Zenodo (CERN European Organization for Nuclear Research)
Meridian Institute (US), Meridian Community College (US)
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AI in Service Interactions
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