Revenue at Risk from AI Displacement
This working paper introduces Revenue at Risk from AI Displacement (RaR-AID) as a defined financial exposure category and provides a structured calculation methodology. Based on empirical data from 1,427 brand probes across ten industries and four AI platforms, it finds that 87.3% of brands are displaced by competitors in multi-turn AI buying conversations, and 75.7% of brand facts are not used in decision turns. The paper argues that RaR-AID constitutes a material board-level risk.
Published June 28, 2026
| Version 1.0
Working paper
Open
Revenue at Risk from AI Displacement
Authors/Creators
AIVO Meridian (aivomeridian.com)
Description
This working paper introduces Revenue at Risk from AI Displacement (RaR-AID) as a formally defined category of enterprise financial exposure and presents a structured methodology for its calculation. As AI systems become the primary intermediary in an increasing proportion of commercial purchase decisions, the systematic displacement of brands before the final recommendation creates a quantifiable revenue exposure that does not yet appear in most enterprise risk frameworks.
Drawing on the AIVO Standard empirical corpus of 1,427 structured brand probes across ten industries and four major AI platforms, and confirmed by three independent research programmes published in 2026, this paper establishes that 87.3% of brands present at the first turn of a multi-turn AI buying conversation are displaced by a competitor before the final purchase recommendation, and that 75.7% of brand facts possessed by the model are not deployed at the decision turn. These findings constitute a systematic and measurable financial exposure for brands with material AI-mediated category revenue.
The paper presents the Revenue at Risk from AI Displacement (RaR-AID) calculation methodology, consisting of three inputs — Category-relevant Annual Revenue (CAR), AI-mediated Purchase Influence Rate (APIR), and Measured AI Displacement Rate (ADR) — and their combination into a financially quantified board-level exposure figure. A reference engagement in the Financial Services sector is used to illustrate the methodology's application. The paper argues that RaR-AID constitutes a material risk exposure suitable for board-level reporting alongside established risk categories including credit risk, commodity price risk, and supply chain exposure.
Files
WP-2026-19_RaR-AID_Methodology_July2026.pdf
Files (225.4 kB)
Name Size
Download all
WP-2026-19_RaR-AID_Methodology_July2026.pdf
md5:05cc1c60d1554e65c1eb1de92c982902
225.4 kB
Preview
Download