Milena Traikovich is a powerhouse in the world of demand generation, recognized for her ability to synthesize complex performance analytics into actionable marketing strategies that deliver high-quality leads. As marketing leaders grapple with the rapid integration of artificial intelligence, her expertise has become a vital compass for those navigating the transition from mere experimentation to sustainable commercial impact. Today, we sit down with her to discuss the launch of the Global Center for AI in Marketing (GCAM) and how its new benchmarking tools are set to redefine how organizations measure their maturity and prioritize their investments in this fast-evolving landscape.
Many marketing departments find themselves trapped in a cycle of isolated AI pilot programs that fail to scale. From your perspective, how does a structured maturity framework help a CMO break out of this “experimentation trap” and start delivering measurable business value?
The experimentation trap is a very real frustration for leaders who feel the weight of executive pressure to innovate but lack the infrastructure to make those innovations stick. What we see is that most organizations—83 percent, to be exact—are currently idling in the early to mid-stages of maturity, often because they focus on the “shiny object” of the tool itself rather than the underlying organizational capability. By using a diagnostic that evaluates an organization across seven critical drivers, including Leadership and Culture and Team Design and Workflow, a CMO can finally stop guessing where the friction is. It provides a structured pathway that turns isolated pilots into responsible, measurable transformation by identifying exactly which foundational gaps are stalling progress. When you have evidence-led insight, you can move away from the noise of “more tools” and focus on the strategic changes that actually move the needle on ROI.
The Global Center for AI in Marketing is expanding its benchmark into the United States through a partnership with The CMO Syndicate. What makes the American market such a critical baseline for this international research, and what should we expect from the findings unveiled at Georgetown University?
The United States represents a unique intersection of massive technology investment and high-stakes operating realities, making it the perfect inaugural international benchmark market. Through our partnership with The CMO Syndicate, we are ensuring that the data we collect reflects the actual strategic priorities of senior American marketing executives across a wide range of industries. When we unveil these findings at Georgetown University in the final quarter of this year, we expect to see a clear baseline that allows for direct comparisons between the US and Australian markets. This isn’t just about who is spending more; it’s about understanding how American leaders are embedding leadership and capability changes to create sustainable value. The goal is to provide CMOs with the credible intelligence they need to strengthen their conversations with boards and executive committees, using the US market’s performance as a high-resolution mirror.
While many studies focus on how much companies are spending on AI, this benchmark looks at “interconnected foundations” like governance and data readiness. Why is it vital to take a technology-agnostic approach when assessing an organization’s AI readiness?
Focusing purely on adoption rates or technology spend is a bit like measuring the speed of a car without checking if it has a steering wheel or a clear destination. A technology-agnostic approach is essential because it allows us to look at the health of the entire ecosystem, specifically through the lens of those seven maturity drivers like Governance, Risk, and Brand, and Data and Technology Readiness. If your governance framework is weak or your team design is outdated, it doesn’t matter how advanced the AI tool is; the output will be inconsistent or even risky for the brand. By assessing whether marketing organizations are building these interconnected foundations, GCAM helps leaders identify if they are truly progressing or simply throwing money at a problem. It’s about building a roadmap that prioritizes human capability and organizational workflow just as much as the silicon and software.
The recent Australian benchmark revealed that not a single organization reached the two highest levels of AI maturity. What does this gap tell us about the current state of marketing leadership, and how does the GCAM diagnostic help bridge it?
The fact that none of the surveyed organizations reached the top two tiers of maturity was a sobering wake-up call, highlighting a massive gap between the pace of AI development and an organization’s ability to actually use it well. It suggests that while marketing teams are eager to experiment, they are hitting a wall when it comes to the complex leadership and operating changes required for full-scale integration. The GCAM diagnostic bridges this gap by mapping results across six distinct maturity levels, giving leaders a visual and data-backed “you are here” marker on their transformation journey. This clarity allows a CMO to say to their team, “We are at level three in data readiness but level one in skills,” which immediately clarifies where the next dollar and hour of training should go. It transforms the vague goal of “doing more AI” into a specific, actionable plan for organizational growth.
What is your forecast for AI maturity in the marketing sector over the next two years as these international benchmarks become more widely adopted?
I expect to see a significant “flight to quality” where organizations stop chasing every new generative tool and start obsessing over their internal “Data and Technology Readiness” and “Governance” drivers. As the benchmark intelligence grows across different sectors and markets, we will see a shift from haphazard experimentation to highly disciplined, evidence-led roadmaps where peer comparison becomes a primary motivator for executive boards. I believe the 83 percent of companies currently stuck in the early stages will begin to bifurcate, with the winners being those who invest heavily in “Team Design and Workflow” to actually integrate AI into their daily operations. By 2028, we will likely see the first cohort of organizations finally breaking into those top two maturity levels, having successfully turned their AI investments into a permanent, measurable competitive advantage.
