AI is increasingly embedded in marketing.
The 2026 CMO Survey reports that AI use in marketing has more than doubled in two years, with generative AI growing faster still, and marketers now expect AI to power the majority of all marketing activities within three years. The same study cites the leading AI use cases as content creation (73.9%) and content personalization (65.4%).
Scott Brinker's Martech for 2026 report shows the same pattern in AI agents specifically: content production leads at 68.9%, with audience segmentation (40.8%) and competitive analysis (35.9%) well behind.
But adoption isn't the same as readiness.
Gartner's 2026 CMO Spend Survey found that while 70% of CMOs say becoming an AI leader is a critical goal, only 30% report mature AI readiness capabilities,
and 70% say their internal marketing processes aren't mature enough to implement and scale AI in the first place.
Whether you view the shift toward AI-driven marketing as a good thing depends on your role, your openness to change, and your overall sentiment toward AI in your personal life.
While AI hasn't been overtly positioned as a quick fix to all of marketing's problems, there's an understated positioning of AI as an "easy button" within the products and services being sold to enterprise marketing teams.
Quick, easy, or otherwise, any software solution that promises transformational benefit carries risk that needs to be thoughtfully considered.
We’re seeing that play out in a wave of public and employee backlash against AI. Consumers are increasingly wary over brands use of AI with 50% saying they would prefer to give their business to brands not using AI. Nearly 55% of workers are bypassing their company’s AI tools and choosing to do their work manually.
If you’ve spent any time reading our content, or if you’ve taken the Marketing Capabilities Maturity Assessment (MCMA) you may have noticed something.
We don’t really talk about AI that much. And that’s deliberate.
In a moment when AI is the default solution recommendation from every martech vendor, and every consulting engagement ends with an AI initiative on the roadmap, we believe senior leaders in scaled marketing organizations need a quieter, more calibrated way to understand how AI fits within their capabilities maturity.
Here’s our perspective.
AI is a tool, not a strategy
That distinction matters more than it sounds. The moment a tool is elevated to the level of strategy, it buckles under the weight of expectations put on it. The focus shifts to the technology itself, and the things that actually make technology deliver (the people, the processes, the operations built around it) get neglected. So the tool gets all the attention, the operations layer never gets built, and the initiative fails. Not because the tool was wrong, but because it was asked to be the strategy, something it was never designed to be.
A lot of advisory firms right now are positioning AI as the answer to marketing's most pressing challenges. They're not always wrong, but when that's the dominant narrative, leaders start reaching for AI on every problem they face. To a hammer, everything looks like a nail. Before long you're running a dozen AI initiatives at once, and you've introduced real operational chaos.
It's worth remembering that marketing was already sophisticated before any of this arrived. You were modeling, segmenting, building audiences, and going to market creatively three years ago. Many of the gains you're after right now are available without AI at all, by consolidating data that's scattered across fifteen systems, or applying a straightforward rules engine. Often that's the cleaner, faster path.
AI is one way to solve a problem
Certain things do become more tractable with AI in the loop: personalization at enterprise scale, content variation across high-volume campaigns, synthesizing signal out of messy customer data. But AI didn't invent those capabilities. They existed before. What AI changes is the economics of doing them.
But it doesn't fix what's broken. For example, a campaign engine that isn't working is usually failing because of process, governance, or data plumbing, not because there's no model in the loop. A personalization program that's underperforming is usually held back by the wrong offer architecture, not unsophisticated targeting. Add AI to a broken process and you simply get a faster broken process.
We don't hesitate to coach marketing leaders to see that the AI initiative they're excited about may be a distraction from reaching the maturity their capabilities genuinely need. That's not skepticism for its own sake. We want to help leaders shift investment into solutions that will actually move the business, and it isn't always AI.
AI raises the value of judgment
The volume of plausible-looking output that internal teams generate has gone up by an order of magnitude. Decks, strategies, roadmaps, and proposals get produced in minutes and land in planning and funding conversations looking entirely credible.
What hasn't kept pace is the capacity to make sense of it. And when there's more on the table than anyone can synthesize, decisions don't speed up, they stall. Decision gridlock results, the exact opposite of the acceleration that AI was supposed to deliver.
There's nothing wrong with wanting to make strategic decisions faster. But getting there in an environment of more content and more tooling requires more humans in the loop, not fewer. It requires the expertise of knowing what to trust, what to ignore, what's missing, and which move comes next.
We're not yet in a world where AI can sit with a leader's specific situation, understand the politics of the organization, weigh the trade-offs against real constraints, and then stand beside them through implementation. That gap doesn't close on its own. It's where judgment, and the people who bring it, become the differentiator.
Standing between the poles
AI has quickly become a polarizing topic. Like a lot of public discourse, it's pulled people into two camps at either extreme. On one end, champions are adopting every tool they can get their hands on, as fast as they can. They don't want to get left behind while competitors pull ahead. On the other, skeptics are digging in and pushing back on all of it. They don't want to sink budget and credibility into a technology they think is overhyped.
Our perspective is a stance in the middle of those extremes.
We're all for AI when it's the right solution to undergird a capability, increasing its maturity to deliver on strategic business priorities. But when we see signals that introducing AI would amplify the impact of operational inefficiency, or introduce risks to the business that others can't see, we'll call it out and offer a solution that's more fit-for-purpose in delivering the immediate need while laying the foundation for AI in the future.
