The future of marketing will be defined by how well organizations operate in an AI-mediated world. Consumers are discovering, evaluating, and purchasing through increasingly intelligent systems; attention is fragmented across proliferating platforms; and expectations for relevance, personalization, and immediacy are rising at once. Marketing is no longer confined to campaigns and channels—it is becoming a real-time growth engine that integrates insights, content, commerce, and performance in a continuous loop. In this environment, advantage will accrue to those that can learn faster, personalize at scale, optimize across the full funnel, and design experiences not only for people but also for the AI systems that guide them. The role of the CMO is expanding accordingly—from steward of brand and demand to orchestrator of data, technology, and AI-enabled execution. That kind of execution is no simple task—and marketing organizations understand this better than most. Marketers, after all, have been among the earliest adopters of gen AI, piloting use cases from copy generation to image creation. Many tools have gained traction, yet because they typically solve isolated tasks, the result has been a patchwork of disconnected pilots and systems that increase activity (for example, more early-concept images produced) while delivering few meaningful enterprise-wide benefits. Much of this fragmentation reflects legacy marketing technology architectures—multiple CMS, digital asset management , CRM, and analytics systems that were never designed for real-time agentic workflows or shared data models. It’s the “gen AI paradox”: The technology can increasingly be found everywhere—except on the bottom line. Agentic AI—systems built on foundation models capable of acting and executing multistep processes—has the potential to address this problem because it offers the opportunity for organizations to fundamentally transform the way work gets done. Rather than relying on practitioners using isolated tools to boost individual productivity and effectiveness, organizations can create hybrid human–agentic workforces—in which people design and oversee networks of AI agents that handle most of the execution. In this model, one marketing professional can supervise a team of agents, potentially driving growth, boosting productivity, and freeing human colleagues to focus on higher-level tasks like creativity and strategy. Realizing this shift requires a modernized technology foundation: unified identity and data layers, flexible model-serving infrastructure, and content and activation systems that expose reliable APIs for agents to act on. Realizing this potential value is only possible through the reimagining and rebuilding of workflows around agentic AI. This is no simple task, which helps explain why companies so far have struggled to extract significant value from AI agents. Organizations that fail to do the hard work needed to reinvent workflows risk creating suboptimal human–agent collaborations and systems that fall far short of delivering on the technology’s promise. While we are still in the early days of agentic AI, a recipe for how to reimagine and rebuild marketing workflows is emerging. This article will examine the five-step process for creating an agentic marketing workflow. Reinventing marketing workflows with agentic AI 2