AI for CMOs: What to Adopt, What to Ignore

The AI for CMO playbook is not a technology question. It is a prioritisation question. Most senior marketers already have access to AI tools that could genuinely improve how their teams work. The ones who are struggling are not short on tools. They are short on a clear view of where AI creates commercial value and where it just creates activity.

This article is about building that view. Not a list of every AI product on the market. Not a breathless overview of what is coming next. A grounded framework for how CMOs can make better decisions about AI adoption, right now, with the teams and budgets they actually have.

Why Most AI Adoption in Marketing Is Going Sideways

I have seen this pattern before. A new capability arrives, agencies and in-house teams rush to adopt it, and within twelve months there is a quiet reckoning about what actually worked. It happened with programmatic. It happened with marketing automation. It is happening with AI right now, just faster and louder.

The problem is not that AI is overhyped. Some of it genuinely is extraordinary. The problem is that most organisations are adopting it without a commercial filter. They are asking “what can we do with AI?” when they should be asking “what business problem does this solve, and is it a problem worth solving?”

When I was running agencies, the tools that delivered real value were rarely the most sophisticated ones. They were the ones that removed a specific bottleneck the team was actually hitting. The rest sat in dashboards, underused and eventually unjustified. AI adoption in 2025 has the same risk profile. The CMOs who will get real returns are the ones who stay commercially grounded while everyone else chases the demo.

If you want a broader view of how marketing leadership is evolving alongside these pressures, the Career and Leadership in Marketing hub covers the structural challenges CMOs are handling right now, from board relationships to remit creep to measurement.

Where AI Creates Real Value for Marketing Leaders

Let me be specific, because vague enthusiasm is useless at this level. There are four areas where AI is delivering genuine commercial value for marketing teams right now, not in theory, in practice.

Content Production at Scale

This is the most obvious one, and it is real. AI-assisted content production is not about replacing writers. It is about changing the ratio of output to headcount. A content team that previously produced eight pieces a month can produce twenty-five, if the briefing, editing, and brand governance processes are tight enough to support that volume without quality dropping.

The constraint is not the tool. It is the brief. AI produces content that reflects the quality of the input it receives. If your brand positioning is fuzzy, your tone of voice guidelines are vague, or your editorial standards are inconsistently applied, AI will produce more content that has all of those problems. The teams getting the most from AI content tools are the ones who invested in sharper creative infrastructure first.

For CMOs thinking about organic content and LinkedIn as a distribution channel, AI can accelerate the drafting and repurposing cycle significantly. But someone still needs to make the judgment call about what to say and why. That is not a task you can automate.

Audience and Competitive Intelligence

This is the area I find most interesting from a strategic standpoint. AI tools are now capable of synthesising large volumes of qualitative data, customer reviews, support transcripts, social listening, competitor positioning, and surfacing patterns that would take a human analyst weeks to identify.

Earlier in my career, I spent a lot of time and budget on lower-funnel performance channels because the attribution was clean and the reporting was satisfying. What I eventually understood was that much of what we were crediting performance marketing for was demand that already existed. We were capturing intent, not creating it. The real growth came from understanding audiences well enough to reach people who did not yet know they needed what we were selling.

AI-powered audience analysis tools are making that kind of insight more accessible. Not perfect, but meaningfully better than what was available five years ago. For CMOs who want to make a credible case for brand investment alongside performance spend, this kind of intelligence is useful evidence.

Reporting and Performance Summarisation

Reporting takes an absurd amount of senior marketing time. I know this from running agencies where account directors were spending a third of their week pulling numbers together and formatting slides that nobody looked at carefully enough to justify the effort. AI can compress that cycle significantly.

The value here is not just efficiency. It is attention. When your team is spending less time assembling reports, they have more time to interpret them. That is a meaningful shift. The CMO who receives a well-structured AI-generated summary of campaign performance can spend their time on the “so what” rather than the “what.” That is where strategic value gets created.

One caution: AI-generated reporting can create a false sense of precision. Analytics tools are a perspective on reality, not reality itself. The numbers that come out of any automated reporting system reflect the data that went in, the attribution model that was applied, and the assumptions baked into the platform. Those are human decisions, and they need human scrutiny.

Paid Media Optimisation

The major platforms have been integrating AI into their bidding and targeting systems for years. This is not new. What has changed is the degree of control that advertisers have ceded to platform algorithms, and the sophistication of the tools available for managing that relationship.

For CMOs overseeing significant paid media budgets, the question is not whether to use AI-driven optimisation. You are already using it whether you know it or not. The question is how much visibility and control you are maintaining over the inputs, the audience signals, the creative, the bidding strategy, and the exclusion lists. Paid search has always rewarded structured thinking more than platform defaults, and that has not changed.

The teams that get the most from AI-driven paid media are the ones who treat the algorithm as a junior team member. You give it clear objectives, good inputs, and guardrails. You do not hand it the keys and walk away.

What CMOs Should Not Delegate to AI

This is where I want to be direct, because there is a lot of noise in the market suggesting that AI can eventually handle most of what a CMO does. I do not think that is right, and I think acting on that assumption will cause real damage to marketing functions that are already under pressure to justify their existence.

Brand judgment is not automatable. Knowing when a piece of creative is off-brand, when a campaign idea is clever but commercially pointless, or when a messaging strategy is technically correct but tonally wrong for the moment, that requires accumulated context, stakeholder understanding, and commercial instinct. AI can produce options. It cannot make the call.

Stakeholder relationships are not automatable. The CMO’s credibility with the CEO, the CFO, and the board is built through consistent judgment over time. AI can help you prepare for those conversations. It cannot have them for you, and it cannot build the trust that makes those conversations productive.

Strategic framing is not automatable. The decision about which market to prioritise, which customer segment to build around, or which brand position to defend under competitive pressure, these are judgment calls that require understanding of the business that no AI system currently has. BCG’s work on digital transformation has consistently shown that the organisations that struggle most are the ones that automate execution before they have clarity on strategy. That pattern holds for AI adoption in marketing.

Building an AI Governance Framework Without Bureaucracy

One of the things I learned from turning around loss-making agencies is that the businesses in trouble were not usually short on ideas. They were short on operational discipline. The same principle applies to AI adoption. The CMOs who will get sustainable value from AI are the ones who build lightweight governance structures before they scale usage, not after something goes wrong.

This does not need to be a 40-page policy document. It needs to answer four questions clearly.

First, what data can be fed into AI tools? Customer data, proprietary research, and commercially sensitive information need explicit rules. Most enterprise AI tools have data handling terms that are worth reading carefully before your team starts pasting client briefs into them.

Second, what output requires human review before publication? The answer for most teams should be: everything that carries the brand. That is not a burden, it is a basic quality control.

Third, how are AI-generated claims verified? This is particularly important for content that makes factual assertions. Moz’s work on content credibility points to accuracy as an increasingly significant factor in how content is evaluated, both by audiences and by search systems. AI hallucination is a real risk, and the brand takes the hit, not the tool.

Fourth, who is accountable for AI-assisted work? The answer should be the same person who would be accountable if the work had been done without AI. The tool does not absorb responsibility.

How to Build the Business Case for AI Investment

CMOs are being asked to justify AI investment at the same time as they are being asked to justify overall marketing spend. That is a difficult position, and vague claims about efficiency gains will not survive a CFO conversation.

The strongest business cases I have seen for AI adoption in marketing are built around three things: a specific workflow that is currently slow or expensive, a measurable output that AI can improve, and a baseline that makes the improvement visible. That is it. You do not need a transformation narrative. You need a before and after that a finance director can follow.

Early in my career, I wanted budget for a new website and was told no. Rather than accepting the constraint, I taught myself to code and built it. The lesson I took from that was not that you should always find a workaround. It was that the clearest path to getting resources is demonstrating value with what you already have. The same logic applies to AI. Start with one workflow, prove the return, and build from there. Asking for a broad AI transformation budget without evidence is a harder sell than asking for a six-month pilot with a specific team and a specific metric.

Getting internal buy-in for new initiatives is a skill that does not get enough attention in marketing leadership conversations. The CMOs who are winning AI investment are the ones who have done the work to connect the tool to the commercial outcome, not just the marketing outcome.

The Team Capability Question

AI adoption is a team capability question as much as a technology question. The tools are only as useful as the people using them. That means CMOs need to think about upskilling, not just procurement.

The most valuable skill in an AI-augmented marketing team is not prompt engineering. It is editorial judgment. The ability to look at AI output and know whether it is good, whether it is accurate, whether it sounds like the brand, and whether it is actually saying something worth saying. That skill is developed through years of working with content, not through a training course on AI tools.

CMOs who are building teams for the next three years should be thinking about how to develop that judgment across the team, not just at the senior level. The writers, strategists, and analysts who can work effectively with AI, directing it, editing it, and knowing when to override it, are the ones who will be most valuable. Strong editorial standards are not a constraint on AI adoption. They are what makes AI adoption work.

For teams managing social content and distribution alongside AI-generated production, understanding the social management tool landscape is worth the time, particularly as AI features are being integrated into those platforms at pace.

The Honest Assessment

AI will not fix a weak marketing strategy. It will not resolve unclear positioning, poor audience understanding, or a dysfunctional relationship between marketing and the rest of the business. What it will do, when applied with discipline, is make a competent marketing team meaningfully more productive and give a well-structured CMO better information to work with.

I have judged the Effie Awards and spent time looking at what actually drives marketing effectiveness across industries. The consistent finding is that effectiveness comes from clarity of strategy, consistency of execution, and genuine audience understanding. AI can support all three of those things. It cannot substitute for any of them.

The CMOs who will look back on this period positively are the ones who stayed grounded, adopted selectively, governed sensibly, and kept their attention on the commercial outcomes that actually matter. The ones who will struggle are the ones who treated AI adoption as a performance in itself, something to be seen doing rather than something to extract value from.

More on the leadership challenges CMOs are managing alongside AI adoption, from measurement to board dynamics to team structure, is covered across the Career and Leadership in Marketing hub. It is worth bookmarking if you are working through any of these questions.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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