This article was based on the interview with Elizabeth Maxson, CMO at Contentful by Greg Kihlström, AI and Marketing Operations keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
The current discourse around AI in marketing often feels uncomfortably binary. Depending on who you ask, we are either on the cusp of a utopian era of unprecedented efficiency or staring into the abyss of mass job replacement. It’s a narrative of extremes. As seasoned leaders, we know the reality is—and always will be—far more nuanced. The relevant question isn’t if AI will fundamentally change our function, but how we, as leaders, will architect that change within our organizations. The most forward-thinking are looking past the simple automation of tasks and toward the augmentation of talent, judgment, and creativity.
This is the central theme of a recent study from Contentful and The Atlantic’s research division, aptly titled “When Machines Make Marketers More Human.” The premise itself challenges the prevailing doom-and-gloom narrative. To explore this, we’re drawing on a conversation with Elizabeth Maxson, CMO at Contentful, who unpacks the findings and offers a grounded perspective on what it takes to build a marketing organization that thrives in the age of AI. We’ll move beyond the hype to discuss how AI can amplify human creativity, why building the right culture is more important than buying the right tool, and what the marketing team of the near future actually looks like.
From Automation to Augmentation
The initial wave of AI adoption has been, understandably, focused on efficiency. Automating mundane tasks, speeding up content creation, and analyzing data sets at scale are tangible, immediate benefits. Yet, focusing solely on this “faster and cheaper” narrative misses the true strategic value. The real opportunity lies in reinvesting the time and cognitive bandwidth saved by AI into the uniquely human aspects of marketing that machines cannot replicate. Maxson sees this as the core of the shift.
“The more that AI can handle the mundane tasks that marketers don’t want to be doing, that really gives them that time back to be more human and really driving creativity, empathy, and even judgment, and gives them really that flexibility and capacity to have that strategic thinking and building creative campaigns.”
This framing is critical for any leader navigating this transition. It’s a shift from seeing AI as a replacement for human effort to seeing it as an amplifier for human intellect. When your team isn’t spending hours pulling logos for a presentation or writing 15 variations of the same social media post, they have the space to ask bigger questions. What is the real customer insight behind this campaign? Is this creative emotionally resonant? Does this strategy align with our long-term brand goals? The danger for marketing leaders is getting trapped in a cycle of measuring AI’s success by sheer output. The true measure of success will be the quality of strategic and creative thinking that it unlocks.
Culture is the Operating System for AI
Having the right mindset is one thing; operationalizing it is another. Many organizations are focused on the tools—which generative AI platform to license, which co-pilot to integrate. While important, this focus often overshadows a far more critical component: the organizational culture. Without a culture that supports learning, experimentation, and psychological safety, even the most advanced AI tools will fail to deliver on their promise.
“Honestly, it really starts with driving a proper AI culture. That’s the one thing I don’t think enough leaders are talking about right now. They’re very focused on the tools… And I don’t think anyone’s spending enough time actually looking, what is the culture that you’re trying to drive in your organization?”
Maxson shared a practical example of this principle in action at Contentful: an internal Slack channel called the “AI Prompt Playground.” It’s a space for employees to share what’s working, what isn’t, and what they’re learning. She recounted a story where a new leader used AI to quickly generate the infamous “NASCAR slide” of customer logos—a task familiar to anyone who has ever tried to build a sales deck. The AI’s output used the wrong logos. Instead of being a failure, it became a catalyst. The initial prompt sparked a conversation with the customer advocacy team and led to a more robust, systemic solution: building a data connector to Salesforce to pull approved logos automatically.
This small anecdote holds a powerful lesson for leaders. The value wasn’t in the initial, flawed AI output. It was in the collaborative, iterative problem-solving it enabled. An effective AI culture isn’t one that expects perfect outputs from a machine. It’s one that uses AI as a starting point to ask better questions, challenge assumptions, and bring the right human experts into the loop. It requires moving beyond mandatory training modules and creating a living, breathing environment of shared learning.
The Dawn of Evidence-Based Creativity
So, with more time afforded by automation and a culture that encourages smart experimentation, what do teams actually do? They get smarter about creativity. For decades, data and creativity have often existed in separate spheres within marketing organizations. Data was used as a rearview mirror—to measure the performance of a campaign after it ran. AI is rapidly breaking down that wall, enabling a powerful fusion of insight and imagination that Maxson calls “evidence-based creativity.”
“What marketers need to start doing is really start with data to inform their campaign strategy… and then allowing the humans to really bring the creativity to drive that and really differentiate yourself from this AI slop that is just happening all over the place.”
This isn’t about using AI to write a generic blog post. It’s about using AI-driven insights to inform the entire creative process from the very beginning. Maxson shared another simple yet profound example: the Contentful web team noticed that 23% of website visitors immediately clicked the “login” button. The data suggested these were existing customers. So why was the homepage serving them “why buy Contentful?” messaging? This single insight triggered a strategic shift to personalize the homepage experience for returning customers, offering them content about new features and adoption rather than acquisition.
This is the essence of evidence-based creativity. It’s not about massive, complex data science projects. It’s about leveraging accessible insights to make smarter creative and experiential choices. It requires marketers who are fluent in both the language of data and the art of brand storytelling—the true “full-stack marketer.” Leaders can foster this by breaking down the silos between analytics, digital, and creative teams, encouraging them to use data not just for reporting, but for inspiration.
Closing the Optimization-Execution Gap
The intent is clearly there. The Contentful study found that 96% of CMOs are prioritizing AI adoption. However, intention doesn’t always translate to action. The same study revealed a significant gap between ambition and reality, one that many of us likely recognize from our own organizations.
“We have found that 96% of CMOs are prioritizing AI adoption. However, only 65% are actually putting meaningful investment behind it. And I think that’s really the gap there… a lot of marketers are still stuck in this experimentation phase. And they need to start thinking about how are they going to deeply embed AI into their workflows.”
This “optimization-execution gap” highlights the risk of perpetual experimentation. Pilots and proofs-of-concept are valuable, but at some point, they can become a form of strategic procrastination. Moving from the experimental phase to true operational integration requires meaningful investment in not just technology, but in people and process. This is reflected in the new types of roles emerging within marketing teams. Maxson mentions hiring an AI Engineer to focus on the marketing tech stack and notes a peer who created a “Content Automation Strategist” role. These aren’t peripheral functions; they signal a deep commitment to embedding AI into the core marketing engine. For leaders, bridging this gap means making tough choices about where to invest and having the conviction to move beyond small-scale tests to foundational, operational change.
In the end, the path forward is not about replacing marketers with machines, but about building a new kind of marketing organization. The shift from automation to augmentation, the critical role of a learning culture, and the fusion of data and creativity are not merely trends; they are foundational pillars for the modern marketing function. The most difficult and important work for leaders will not be mastering the art of the prompt, but mastering the art of organizational design—architecting a team that can leverage these powerful new tools to become more strategic, more empathetic, and, counterintuitively, more human.
As Maxson notes, the ground is still shifting beneath our feet, with concepts like “Generative Engine Optimization” (GEO) emerging as the next frontier. This is a constant reminder that agility is paramount. The ultimate competitive advantage in the age of AI won’t be a proprietary algorithm or a bigger tech budget. It will be the very human capacity to learn, adapt, and relentlessly pursue a deeper connection with the customers we serve.
