What CMOs get wrong about AI adoption
Most marketing leaders approach AI as a technology decision. The ones making progress treat it as an operational one. Here is where the gap usually opens.
There is a pattern we see with senior marketing leaders and AI. They understand the potential. They have read the reports, seen the case studies, probably experimented with a few tools themselves. But when it comes to actually implementing AI across their function, progress stalls.
Not because of budget. Not because of technology. Usually because the approach itself is off.
Treating AI as a technology decision
The most common mistake is framing AI adoption as a technology question. Which tool should we use? What platform should we invest in? Should we build or buy?
These are reasonable questions, but they are second-order questions. The first-order question is: where does AI fit into how your marketing function actually operates?
When AI adoption starts with technology selection, it tends to produce a tool that sits alongside the existing workflow rather than inside it. Someone gets access to a new platform, uses it for a few weeks, and then quietly stops because it does not connect to the way the team actually works.
Delegating it downwards
The second pattern is delegation without context. A CMO decides the team should adopt AI, assigns it to a mid-level manager or the most technically curious person on the team, and steps back.
The problem is that meaningful AI adoption usually requires decisions about process, priority, and resource allocation that only a senior leader can make. Which workflows matter most? What does success look like? How much time should the team spend learning versus delivering?
Without senior involvement in those decisions, the project tends to drift. It becomes an experiment rather than a commitment. And experiments, by definition, are easy to stop.
Waiting for the perfect moment
Some leaders hold off because the landscape is moving so quickly. Why invest in training now when the tools might be different in six months?
The answer is that the skills and thinking transfer. Understanding how to design an AI workflow, how to brief a system properly, and how to evaluate whether an output is useful — these are durable skills. The tools will evolve. The ability to use them well is what compounds.
Waiting for stability in a fast-moving space means waiting indefinitely. The businesses making progress started imperfect and improved as they went.
Underestimating the skills gap
There is often an assumption that AI tools are intuitive enough that teams will figure them out. And at a surface level, they are. Anyone can log into a chatbot and get a response.
But there is a significant gap between getting a response and getting a useful, reliable, commercially valuable output. That gap is where most marketing teams get stuck. They try the tools, get inconsistent results, and conclude that AI is not quite ready for their use case.
In most cases, the tools are ready. The team has not been shown how to use them properly. This is not a criticism — it is a training problem, and a solvable one. Understanding what AI skills marketers actually need is the first step.
Buying a course instead of building capability
The instinct is often to send people on a course. A day of training, a certification, a series of webinars. These can be useful for awareness, but they rarely change how a team operates.
The reason is that generic courses teach generic skills. They cover the broad capabilities of AI without mapping them to the specific workflows, tools, and challenges your team faces every day. The team comes back with knowledge but no clear path to implementation.
What tends to work better is training that is built around the actual function — the real workflows, the real tools, the real problems. When people build something during training that they can use the next day, adoption sticks. When they sit through slides, it does not.
What the ones getting it right have in common
The marketing leaders we see making genuine progress with AI share a few traits:
They are personally involved. Not doing the technical work, but making the decisions about where AI fits and holding the team accountable for following through.
They start with one workflow, not a strategy document. They pick something specific, build a system that handles it, and use the results to build the case for doing more.
They invest in practical training. Not awareness. Not certification. Hands-on programmes where their team builds real workflows and leaves with something they can use immediately.
And they treat AI adoption as an ongoing process, not a one-off project. The first workflow is the beginning, not the finish line.
The real question
The question for marketing leaders is not “should we adopt AI?” That has been answered. The question is whether you are approaching it in a way that will actually produce results, or in a way that will produce another pilot that quietly disappears.
The difference is usually not about the technology. It is about the decisions being made at the top.