From ranking to representation: Why brand visibility in generative artificial intelligence is a go-to-market strategy

Abstract

The rapid adoption of generative artificial intelligence (AI) systems as discovery interfaces is reshaping how brands are surfaced, evaluated and compared. Many organisations have approached this shift as ‘SEO 2.0’ (search engine optimisation), attempting to optimise for position within AI-generated responses. This paper argues that such a framing misunderstands the structural mechanics of large language models (LLMs). Drawing on structured practitioner observation conducted since early 2024 across multiple LLM vendors and industry verticals, together with emerging literature on generative engines, this paper argues that generative systems do not rank brands in stable positions but reconstruct markets probabilistically through semantic relationships. Brand visibility is therefore shaped by ontological embedding, attribute association and narrative centrality rather than keyword optimisation alone. To respond to this shift, the paper introduces the generative visibility stack, a cross-functional go-to-market framework designed to strengthen structural brand salience within generative systems. The findings suggest that discoverability in the generative era is a coordinated perception challenge requiring alignment across marketing, sales and ecosystem actors. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business.