AI marketing’s real problem: execution, not imagination
AI marketing execution is the discipline of turning AI demos into repeatable, on-brand workflows that survive real approvals, protect reputation, and deliver measurable value across campaigns and channels. That means less focus on novelty and more on coordination, oversight, and how AI systems describe brands when customers first meet them through machine-generated answers. At this point, the gap between what AI could do for marketing and what teams can responsibly ship has become the main strategic risk. Cannes conversations have moved from "look what this model can do" to "can we trust this in front of a client or regulator?" The winners will not be those with the flashiest pilots, but those who treat AI as an operations problem before they treat it as a creative toy.
Cannes Lions: AI marketing hits workflow friction and human oversight
The signal from Cannes is blunt: AI marketing has an execution problem. The event framed "AI marketing’s challenge" around whether tools can fit into real creative development, approvals, and brand safety expectations, not whether they look impressive on stage. Microsoft, EA, Anthropic, and even Teletubbies were threaded into this conversation, underscoring how much of AI talk is now wrapped in cultural moments as well as tech narratives. In practice, AI campaigns stall because they are coordination problems: creative, media, legal, and data teams lack shared standards for what is acceptable, measurable, and repeatable. When those standards are missing, pilots stay trapped as isolated experiments. If AI touches ideation, production, or optimization, it changes who reviews what and when, so planning has to start with process mapping instead of model selection. Human oversight remains central, and timelines need to acknowledge that.
Brand control in the age of AI customer discovery
While Cannes exposed process pain, Contentful’s Palmata surfaces an equally urgent issue: brand control in AI customer discovery. For more than two decades, marketers built around traditional web search and SEO; the rise of AI-powered search engines has broken that pattern as more users talk to assistants instead of browsing links. As a result, a customer’s first impression of a brand may be an AI summary before they visit a site, creating a fresh visibility and reputation challenge. Palmata’s bet is that brands cannot afford to guess what models say about them. It analyzes publicly available information across a company’s digital footprint to map how it shapes AI-generated answers. Palmata’s Steering Control lets teams target specific products, audiences, and competitors so they can see exactly what AI claims and where it learned it. When brands lose control of first impressions, this kind of tooling aims to "put them back in the driver’s seat" by restoring monitoring and influence over AI-generated descriptions.
From AI SEO to AI reputation management
The shift to AI customer discovery is harsher than the old SEO arms race. In AI search, the model answers with total confidence, whether or not it is right, and customers may never click through to see the nuance. CX is no longer contained within the website; it starts wherever an AI forms an answer. That forces a new discipline: brand control AI, where teams treat AI summaries as a first-touch experience to be managed, not a side effect. Palmata is designed to help organizations understand and improve how AI answer engines represent their brand, explicitly responding to how customers now discover products. By optimizing content for how AI systems interpret and summarize it, brands can improve inclusion and accuracy within generated answers. The tool highlights gaps between intent and how AI describes the business, turning AI discovery risk into a plan for growth by prioritizing content updates and clarifying competitive positioning.
Stop chasing novelty and fix the AI marketing basics
The throughline between Cannes and Palmata is clear: marketers must stop chasing novelty and start fixing the plumbing of AI marketing execution. Event narratives compress complexity into hype-friendly slogans, but inside organizations, AI success depends on governance, documentation, and consistent review standards becoming part of creative credibility. AI should be treated as marketing workflow automation that redefines who does what and how quality is judged, not as a magic layer added on top. Teams need concrete criteria around brand fit, compliance risk, and performance before they scale output. On the discovery side, brands can no longer ignore how AI systems mention them, and how accurately, across channels. Contentful’s move shows that brand leaders are starting to prioritize implementation: building the content structures, monitoring loops, and controls required to influence AI answers. The real competitive edge now lies in executing reliably under constraints, not in having one more demo to show on a festival stage.
