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Drew Neisser is the founder of CMO Huddles and a globally recognized authority on B2B marketing. He’s an AdAge columnist, LinkedIn TopVoice, leading CMO coach, podcast host & friend of penguins everywhere.
Some CMOs are drawing a clear line on AI adoption. Others are wrestling with principled objections from employees. The right answer is not blind enthusiasm or blanket coercion. CMOs need to build a learning culture where AI experimentation is expected, governance is clear, and human judgment remains the standard.
“If you upset one of our salespeople, I will take you out.”
That line came from Denise Persson, CMO of Snowflake, in a conversation about how marketing teams should work with sales. Her point was not subtle. Some relationships inside the company are too important to treat casually.
Lately, I have been wrestling with a different kind of redline: AI adoption.
During four Strategy Labs on the East Coast, I asked roughly 40 CMOs about their AI adoption curves and how tolerant they planned to be of laggards. The answers varied widely, but a few leaders had already drawn a clear line.
In 2026, refusing to learn AI may start to look less like caution and more like refusing to learn the job.
That does not mean this is simple.
Some employees object to AI for moral reasons: Energy and water consumption, copyright concerns, labor implications, or distrust of the companies building the tools. Some leaders have tried to present comparative data showing that LLM queries may use similar or even less energy than certain Google searches. But most agreed that if someone holds a deeply principled objection, data alone probably will not change their mind.
CMOs should take these objections seriously without letting them freeze the organization.
A marketing team that refuses to experiment with AI will fall behind. A marketing team that ignores ethical concerns will lose trust. The leadership challenge is holding both truths at once without needing a fainting couch.
The best marketers I know are experimenting constantly. They are curious. They are learning in public. They are pushing the tools further every week.
That curiosity matters more than tool fluency alone.
A healthy AI culture does not mean everyone uses the same prompt library or worships at the altar of automation. It means teams know where AI is allowed, where it is risky, where human review is required, and which workflows should improve because of it.
The goal is not AI usage for its own sake. The goal is better work, faster learning, and smarter decisions.
CMOs should be explicit.
What AI skills are now expected in each function? What tools are approved? What data cannot be entered? What outputs require review? What ethical concerns deserve discussion? What experiments should every team run this quarter?
Ambiguity is the enemy here. If leaders say “go use AI” without defining the sandbox, teams either freeze or improvise dangerously.
The practical answer is a learning mandate: Every marketer should be expected to learn, test, and responsibly apply AI where it improves the work. Not every task should be automated. Not every objection should be dismissed. But opting out of learning is not a viable long-term position.
That may sound firm. It should.
The future of marketing will not be built by people who refuse to look at the tools changing the work.
Require learning and responsible experimentation, not blind usage. Expectations should vary by role and workflow.
Listen seriously, clarify governance, and distinguish principled concerns from general resistance to change.
Creating tool adoption without judgment. Usage metrics are meaningless if the work does not improve.
Define approved tools, data rules, review standards, and one practical AI workflow each function should test.