Brands urgently need to adopt new strategies built around Generative Engine Marketing (GEM) if they are to thrive in an era in which Large Language Models (LLMs) govern how they are discovered, considered and purchased, according to the latest report from leading digital marketing agency Jellyfish – Brands in the AI Era: Generative Engine Marketing.
Brand strategy faces immense disruption from the AI-driven shift in media technology, as brands race to build relationships not only with people but with the LLMs that surround them. But those that embrace the shift are already beginning to drive powerful results and capitalise on an unprecedented opportunity in marketing and commerce.
GEM represents marketers’ strongest response to the challenge of current and forthcoming AI-driven experiences, the report suggests. It offers a systematic framework to ensure brands are accurately understood and effectively surfaced by AI systems that increasingly act as gatekeepers, audiences and buyers.
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“Brands have historically been made by people, for people,” says John Dawson, Vice President of Strategy, Jellyfish, USA and a co-author of the report. “Now, AI is an actor in the system: watching, recommending, choosing and even purchasing. The crucial task for brands to address is how to adapt.”
A GEM approach allows brands to identify the true potential of AI in today’s ‘ambient’ era of agentic shopping, multimodal search, embedded transactions and conversational queries, where AI agents, LLMs and search models will increasingly be delegated by humans – and sometimes bypass humans entirely.
GEM is an entire marketing system that allows brands to develop end-to-end brand strategies for the AI era. Building on the Jellyfish Share of Model™ Platform – which enables companies to analyse how LLMs perceive their brands, products and services – GEM provides a continuous loop of technical optimisation, content creation, pre-testing, distribution and measurement, to ensure that brand assets are interpretable by models, resonant to audiences (human and AI), and optimised through ongoing feedback.
“The brands that succeed in this emerging era will not simply advertise to audiences but train the models that mediate them,” says Dawson. “They will know their challenges and opportunities through Share of Model™, manage their semantic presence and shape how AI describes their world. To do that requires more than optimisation; it demands orchestration, connecting technical, creative and strategic functions into a single, generative system.”
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Brands are already seeing immediate returns on investment from GEM strategies, with double-digit uplifts in key metrics from minimal increase in workloads or organisational investment.
Gentle Monster, the avant-garde Korean eyewear brand, used LLM insights to optimise its Google Performance Max campaigns before the US holiday season, generating a 17 per cent improvement in click-through rate, a more than 14 per cent uplift in conversion rate, AND a more than 39 per cent improvement in return on ad spend. Industrial maintenance, repair, and operations company MSC Industrial drove a 45 per cent increase in revenue in the first 30 days of implementing AI-driven optimisations to its Performance Max campaigns, delivering a 758 per cent incremental ROAS.
“We have seen several major media technology shifts in the past 20 years, from the first wave of digital and the rise of social to the growth of mobile,” says Dawson. “But this time, the shift really is different. For the first time, brands must build relationships not only with people but also with the AI models that surround them.
“GEM reframes this challenge as an opportunity: to design brands that are legible to machines, resonant to humans, and optimised for both. It extends the marketer’s craft beyond campaigns into the underlying data, structures, and signals that inform AI understanding. AI does not end the story of marketing, but it does open a new chapter, and GEM provides a canvas on which it can be crafted.”
Jellyfish, is the integrated global digital marketing business.
