How Marketers Can Influence AI Chatbots: Lessons From Cannes Lions

Marketers at the Cannes Lions festival are shifting their focus from traditional Search Engine Optimization (SEO) toward influencing the responses of generative AI chatbots, according to reporting by The New York Times. This strategy, which seeks to ensure brands are cited and recommended by large language models (LLMs), represents a transition from driving clicks to securing inclusion in AI-synthesized answers.

The discussion at the festival centers on the belief that traditional digital marketing is insufficient for the era of generative AI. While SEO focuses on ranking high in a list of search results to attract human clicks, the new objective is to appear as the primary recommendation when a user asks a chatbot for a product or service suggestion.

How does generative engine optimization differ from SEO?

According to The New York Times, the primary difference lies in the user’s journey. Traditional search engines provide a directory of links, allowing advertisers to compete for the top spot through keywords and paid placements. In contrast, AI chatbots like ChatGPT, Gemini, and Perplexity synthesize information into a single, cohesive answer, often omitting the long lists of links that advertisers have spent decades optimizing.

This shift creates a “winner-take-all” dynamic. If a chatbot recommends only one or two brands for a specific query, brands that are not included in that synthesis lose visibility entirely. Marketers are now grappling with how to influence the “seed” data and the training sets that these models use to determine which brands are authoritative or popular.

What strategies are marketers using to influence AI chatbots?

Industry professionals are exploring several methods to increase their visibility within AI outputs, as detailed by The New York Times:

• Authoritative Citations: Increasing the number of mentions in high-authority publications and forums that AI models frequently scrape for training data.
• Structured Data: Using technical schemas to make brand information more legible and “digestible” for AI crawlers.
• Sentiment Management: Focusing on the qualitative nature of mentions, as LLMs often synthesize the general sentiment of the web to decide which product is “best.”
• Direct Partnerships: Exploring potential payment models or partnerships with AI platform providers to ensure brand presence.

This approach moves marketing away from quantitative metrics, such as the number of backlinks, toward qualitative influence. The goal is to be perceived by the AI as the most reliable or frequently cited answer to a specific consumer problem.

What are the business risks of AI-driven discovery?

The transition to AI discovery introduces significant risks for corporate brand safety and revenue stability. One primary concern is the tendency of LLMs to “hallucinate,” or invent facts, which could lead a chatbot to associate a brand with incorrect information or attribute a competitor’s features to a different product.

Furthermore, the lack of transparency in how AI models weight certain sources makes it difficult for companies to measure the return on investment (ROI) for these new optimization efforts. Unlike traditional search, where a brand can track its rank for a specific keyword, AI responses are stochastic and can change based on the phrasing of the prompt.

This creates a contrast in how brands manage their digital presence. In the SEO era, brands controlled their narrative by optimizing their own websites. In the generative AI era, the narrative is controlled by the model’s interpretation of third-party data across the wider web.

Why does this change the advertising model?

The emergence of AI-driven answers threatens the traditional “cost-per-click” (CPC) revenue model that has dominated the internet for two decades. Because chatbots often provide the answer directly within the interface, users have less incentive to click through to a brand’s website.

This development forces a reconsideration of how brands measure success. If a user discovers a product through an AI chatbot but does not click a link, the brand gains awareness without a trackable digital trail. Marketers are now tasked with finding new ways to attribute sales to AI recommendations, a challenge that was not present in the traditional search-and-click ecosystem.