AI marketing’s real problem: execution, not imagination
AI marketing execution is the practical discipline of turning model-powered ideas into repeatable workflows that produce on-brand, measurable customer experiences under real operational constraints. That means moving from impressive demos to systems that fit approval processes, protect brand reputation, and adapt to how customers now discover products through AI. At Cannes Lions, the conversation shifted from novelty to this reality: AI marketing is being judged by whether it can work inside actual creative development, legal review, and brand safety standards, not by how clever a one-off activation looks.
This is why so many “AI-powered” campaigns stall. Marketers are sold automation, but they inherit coordination problems. Creative, media, legal, and data teams all need shared rules for what AI is allowed to say and ship. When those rules are missing, pilots remain isolated experiments, no matter how polished the conference case study appears. The gap between AI marketing hype and results is now less about technology and more about the workflows wrapped around it.
Cannes Lions: from sizzle reels to workflow friction
The mood at Cannes Lions Day 3 made that shift explicit. Big names like Microsoft, EA, Anthropic, and even Teletubbies surfaced in a discussion less about spectacle and more about the constraints that shape AI marketing in practice. This was not an AI showroom; it was a therapy session on approvals, governance, and the limits of automation.
In practice, AI marketing execution has become a coordination challenge. If AI touches ideation, production, or optimization, it changes who reviews what and when; planning should start with process mapping, not model selection. When teams lack shared definitions of what is acceptable, measurable, and repeatable, workflow automation challenges appear fast: pilots stall, outputs sit in legal queues, and no one can say if “quality” has improved. Oversight remains central: humans still define briefs, guardrails, and final approvals even when AI accelerates production. The result is that execution constraints, not model capabilities, now set the ceiling on what AI marketing can claim.
AI discovery changed the rules of brand reputation
While Cannes wrestled with workflows, another shift is reshaping marketing AI implementation: AI-powered search engines are now often the first touchpoint in the customer journey. For more than two decades, brands built their digital playbooks around traditional web search and SEO. Now, users increasingly start with AI assistants instead of browsing links, so a customer’s first impression can be formed by an AI’s summary before they ever visit a site.
That makes brand reputation AI a frontline concern, not a side project. Due to LLM complexity, organizations must know if and where AI systems mention their brand and how accurately they describe it. Contentful has today launched Palmata, a software designed to help businesses understand and improve how they appear in AI search engines. Using its Sounder Discovery Agent, Palmata analyzes publicly available information across a company’s digital footprint to map how it influences AI-generated responses. This is execution work, not theory: structured, reusable content, consistent messaging across channels, and ongoing monitoring now decide how AI presents a brand.
Closing the gap: from AI hype to controlled execution
The next phase of AI marketing will be won by teams that treat AI as a workflow change and a discovery risk, not as a gadget. Cannes conversations are only useful if they turn into decision-ready frameworks with clear governance. Marketers need to make “quality” measurable before scaling: agree on criteria like brand fit, compliance risk, and performance deltas rather than arguing over vibes. The broader signal is that AI is moving from curiosity to accountability; as it becomes more visible in flagship campaigns, it becomes easier to challenge, audit, and compare across efforts.
On the discovery side, tools like Palmata put brands back in the AI driver’s seat. Palmata helps organizations turn AI discovery risk into a credible plan for growth by giving teams the clarity and intelligence they need to understand their AI reputation and improve it over time. Teams can focus on specific markets and competitors, generate prioritized recommendations for campaign changes, and simulate how those changes might affect future AI-generated answers. For CX leaders, consistent monitoring of AI systems is now part of brand safety: it lets them manage the distributed information footprint that shapes AI perceptions before customers engage directly. In this world, the advantage is less about having access to AI and more about reliably producing on-brand work under tight timelines and real constraints.
