AI-Based Search: Information-Search Marketing and Generative Engine Optimization
This technical note explains the evolution of consumer search behavior and introduces a practical framework that can enable marketing leaders to manage brand visibility when customers increasingly receive answers from AI rather than lists of links from a traditional search platform. The note describes the meaning and emergence of terms like Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM), introduces a framework for prioritizing investments and managing risk in Information Search Marketing (ISM), and provides a short reflective review for leaders who must navigate a landscape of both traditional and AI-based search platforms.
Collection: Darden University of Virginia (USA)
Ref: DARDEN-M-1083-E
Format: PDF
Number of pages: 9
Publication Date: May 5, 2026
Language: English
Description
This technical note explains the evolution of consumer search behavior and introduces a practical framework that can enable marketing leaders to manage brand visibility when customers increasingly receive answers from AI rather than lists of links from a traditional search platform. The note describes the meaning and emergence of terms like Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM), introduces a framework for prioritizing investments and managing risk in Information Search Marketing (ISM), and provides a short reflective review for leaders who must navigate a landscape of both traditional and AI-based search platforms.
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