Culture & Influence

Best Generative Engine Marketing Strategies for Brands in the AI Era

Thursday, 7 May 2026 9:10AM UTC

Shoppers and search engines are being joined by AI agents, so brands are racing to be understood by machines as well as people; here’s why Generative Engine Marketing matters, how it works, and practical steps to get started.

Essential Takeaways

GEM defined: Generative Engine Marketing (GEM) is a systemised approach to make brands legible to large language models and AI agents as well as human audiences.
Performance wins: Early adopters report double-digit uplifts in CTR, conversion rates and ROAS with modest extra investment.
Core loop: GEM depends on continuous optimisation, data, content, pre-testing, distribution and measurement that teach models what a brand means.
Why it matters now: AI-driven discovery and agentic shopping are reshaping how customers find and buy, so brands face both risk and opportunity.
Practical start: Audit your “semantic presence”, map model touchpoints and prioritise content and technical signals that AI reads easily.

Why brands must train AI as well as persuade people

The biggest change isn’t just smarter search, it’s that AIs are becoming intermediaries that watch, recommend and sometimes buy on behalf of people, and that shifts the target of marketing from humans alone to models too, a point Jellyfish makes strongly. That feels strange at first, your brand now needs to smell right to a model, but the upside is clear: be easy for models to understand and you get found more often. According to industry reporting, many brands aren’t yet ready for this transition, which makes early action a competitive edge.

What Generative Engine Marketing actually looks like in practice

GEM is less a single tactic and more a closed loop: measure how models perceive your brand, create content and data structures that align with those perceptions, pre-test with model simulations, distribute across channels, then measure and refine. Practical elements include structured data, template-rich creative, and tests that check how an LLM describes your product. Companies that tried this are already seeing notable lifts in campaign metrics, so it’s not theoretical, it’s operational.

Real results: when optimisation meets model-awareness

Case studies show the theory works. Brands that used LLM insights to tweak search and performance campaigns saw material improvements in click-through, conversions and return on ad spend within weeks. Those are the kind of quick wins that justify a GEM pilot: small changes to descriptions, metadata and creative framing can make your product more discoverable in AI-driven journeys. Industry analysis suggests those performance uplifts are going to become standard expectations as more buying paths are agent-mediated.

The risk of falling behind (and how most brands stack up)

Market research indicates that a majority of brands still lack the capabilities to handle AI-led discovery end to end. That’s risky because the platforms and models shaping discovery reward clarity and consistent signals; brands with fragmented data and inconsistent messaging risk being misrepresented or overlooked. The smart move is to map where models touch your funnel, search, chat, recommendation engines, and prioritise fixes where misinterpretation would cost the most.

How to begin building a GEM system without breaking the bank

Start small and practical: run a Share of Model-style audit to see how models describe you, then fix the low-hanging fruit, structured product data, clearer product descriptions, and a handful of model-focused content tests. Connect technical, creative and analytics teams into a single workflow so learnings flow quickly. You don’t need a full organisational overhaul to see results; pilots focused on high-value categories or holiday windows can prove the approach and fund wider roll-out.

Looking ahead: what success with GEM will feel like

Brands that master GEM will be those that think in dual audiences, people and the models that mediate them, so success will look like steadier discovery, more predictable conversions and campaigns that compound rather than burn out. It’s an invitation to extend marketing craft into the data and signal design that inform AI. For marketers, that’s both a creative challenge and a fresh playground.

It's a small shift in focus but one that could change who finds your brand and how they decide to buy.

Source Reference Map

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More on this

https://martechvibe.com/article/generative-engine-marketing-emerges-as-key-strategy-for-brands-in-ai-era/ - Please view link - unable to able to access data
https://www.adtechjuice.com/new-jellyfish-report-introduces-generative-engine-marketing-as-a-compass-for-brands - A recent report by Jellyfish introduces Generative Engine Marketing (GEM) as a strategic framework for brands navigating the rise of Large Language Models (LLMs). The report highlights the need for brands to build relationships not only with consumers but also with AI systems that influence discovery and purchasing decisions. GEM offers a systematic approach to ensure brands are accurately understood and effectively surfaced by AI systems acting as gatekeepers, audiences, and buyers. ([adtechjuice.com](https://www.adtechjuice.com/new-jellyfish-report-introduces-generative-engine-marketing-as-a-compass-for-brands?utm_source=openai))
https://www.bcg.com/publications/2024/brands-must-adapt-as-genai-evolves-product-discovery - An article by Boston Consulting Group discusses how generative AI is revolutionising product discovery, urging brands to adapt to this shift. Consumers are increasingly using chatbots and search engines powered by generative AI to find new products and services. Brands, especially in sectors like autos, finance, retail, and travel, need to prepare for this landmark change. The article emphasises the importance of understanding how LLMs provide hyperpersonalised answers and focused recommendations, compressing the discovery-to-decision process. ([bcg.com](https://www.bcg.com/publications/2024/brands-must-adapt-as-genai-evolves-product-discovery?utm_source=openai))
https://www.emarketer.com/content/most-brands-arent-ready-ai-driven-discovery - A report from eMarketer reveals that most brands are unprepared for AI-driven discovery. Despite the rising use of AI agents for search, shopping, and discovery, 47% of brands have no deliberate Generative Engine Optimisation (GEO) strategy or are unaware of their presence in AI agent responses. Another 47% have only just begun optimising content for AI discovery. The report highlights the need for brands to adapt their content and campaigns for AI agents to enhance discovery, engagement, and visibility. ([emarketer.com](https://www.emarketer.com/content/most-brands-arent-ready-ai-driven-discovery?utm_source=openai))
https://www.bain.com/insights/marketings-new-middleman-ai-agents/ - Bain & Company discusses the emergence of AI agents as a new middleman in marketing. As buyers increasingly turn to AI tools for product recommendations, comparisons, and research, the marketing funnel is being upended. AI-powered 'zero-click' journeys compress the discovery-to-decision process, reducing opportunities for brands to influence consumers. The article emphasises the need for brand stewards to learn to communicate effectively with AI agents to maintain influence in the consumer decision-making process. ([bain.com](https://www.bain.com/insights/marketings-new-middleman-ai-agents/?utm_source=openai))
https://www.jellyfish.com/en-gb/blog/brand-discovery-is-being-reshaped-by-ai/ - Jellyfish explores how AI is reshaping brand discovery through Generative Engine Marketing (GEM). The article highlights the shift from traditional search methods to AI-driven discovery, where consumers increasingly rely on AI assistants for product recommendations. Brands are urged to adapt to this change by ensuring their information is well-structured, consistent, and accessible across multiple platforms, including conversational AI, to remain relevant in the evolving digital landscape. ([jellyfish.com](https://www.jellyfish.com/en-gb/blog/brand-discovery-is-being-reshaped-by-ai/?utm_source=openai))
https://www.jellyfish.com/en-us/genai/ - Jellyfish discusses the future of brands in the era of Generative AI (GenAI). The article outlines how brands can progress from experimentation to integration by developing unique brand models maintained by internal or hybrid agency teams. It emphasises the importance of training these models to generate distinctive assets and track category creative norms. The piece also highlights the potential of GenAI to unlock new insights, audiences, and strategic opportunities for brands willing to push beyond initial efficiency gains. ([jellyfish.com](https://www.jellyfish.com/en-us/genai/?utm_source=openai))

Noah Fact Check Pro

The draft above was created using the information available at the time the story first emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed below. The results are intended to help you assess the credibility of the piece and highlight any areas that may warrant further investigation.

Freshness check

Score: 7

Notes: The article was published on May 7, 2026. A similar report from Jellyfish was published on May 6, 2026, introducing Generative Engine Marketing as a compass for brands navigating the rise of Large Language Models (LLMs). ([adtechjuice.com](https://www.adtechjuice.com/new-jellyfish-report-introduces-generative-engine-marketing-as-a-compass-for-brands?utm_source=openai)) This suggests that the content is fresh, but the close proximity of publication dates raises questions about originality.

Quotes check

Score: 6

Notes: The article includes direct quotes attributed to John Dawson, Vice President of Strategy at Jellyfish. However, these quotes are not independently verifiable through other sources, raising concerns about their authenticity. The lack of corroboration from external sources diminishes the credibility of the quotes.

Source reliability

Score: 5

Notes: The article originates from MartechVibe, a publication that appears to be a niche or lesser-known source. While it references a report from Jellyfish, a reputable digital marketing agency, the lack of independent verification and the close timing of publication with other similar reports raise questions about the source's reliability.

Plausibility check

Score: 7

Notes: The concept of Generative Engine Marketing (GEM) aligns with current industry trends, as evidenced by similar reports from Jellyfish and other sources. However, the article lacks specific factual anchors, such as names, institutions, or dates, which diminishes its credibility. The absence of detailed supporting information raises concerns about the article's authenticity.

Overall assessment

Verdict (FAIL, OPEN, PASS): FAIL

Confidence (LOW, MEDIUM, HIGH): MEDIUM

Summary: The article presents a concept that aligns with current industry trends but lacks independent verification and specific factual details, raising concerns about its authenticity and reliability. The close timing of publication with other similar reports and the reliance on a single source without external corroboration further diminish its credibility.