{"id":11229,"date":"2026-07-28T08:28:03","date_gmt":"2026-07-28T14:28:03","guid":{"rendered":"https:\/\/attentionmedia.io\/?p=11229"},"modified":"2026-07-28T08:28:03","modified_gmt":"2026-07-28T14:28:03","slug":"the-cmos-guide-to-building-an-ai-first-marketing-team","status":"publish","type":"post","link":"https:\/\/attentionmedia.io\/?p=11229","title":{"rendered":"The CMO\u2019s guide to building an AI-first marketing team"},"content":{"rendered":"
\"AI-assisted<\/div>\n

Marketing teams are usually the first to test new ways of working: new channels, attribution models, content formats, and growth experiments. AI should follow the same pattern. Paid channels are getting more expensive, budgets are tighter, and teams are under pressure to produce more without adding headcount.\u00a0<\/p>\n

AI fluency isn\u2019t the only answer to that challenge, but it\u2019s one of the most obvious and practical levers available to marketing leaders right now. AI transformation requires building that capability across the team. After doing this inside my own department, I can say the hardest part isn\u2019t choosing the model or buying the seats. It\u2019s changing how a team thinks, briefs, reviews, and ships work.<\/p>\n

Marketing teams are moving faster than most functions, with marketers spending nearly twice as much time<\/a> with AI tools, according to a TMetric study. McKinsey\u2019s State of AI research<\/a> highlights this very gap: adoption is growing, but many companies are still experimenting without changing how work is actually done.\u00a0<\/p>\n

For CMOs, that\u2019s the real challenge now \u2014 not getting people to try AI once, but turning early usage into better workflows, faster execution, and measurable results.<\/p>\n

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CMO\u2019s No. 1 job is to get the team engaged<\/h2>\n

Before any training plan, KPI table, or consultant booking, people need a reason to care. If the team is curious, the training will stick, and experiments will happen naturally. I\u2019d start with visible examples and low-pressure experimentation.\u00a0<\/p>\n

During regular team syncs, ask people what actually worked for them: a workflow that saved time, a research shortcut that improved quality, an agent that almost worked, or a campaign task where AI helped them move faster. Don\u2019t make it only about prompts. Make it about useful cases, lessons learned, and small experiments that others can copy. Celebrate these openly and make it safe to try imperfect things.<\/p>\n

Remove the small blockers that quietly kill AI adoption. Buy strong corporate licenses with enough tokens and usage capacity, so people aren\u2019t counting tokens every time they want to test an idea. <\/p>\n

After three months, once you have a few champions and real project wins, turn the best examples into baseline workflows and core automations. Then it won\u2019t feel like you\u2019re forcing a new process on the team. It\u2019ll feel like you\u2019re formalizing what already works.<\/p>\n

Pair AI training with practice<\/h2>\n

The rule on my team: every training is paired with a real project that starts the same week. Sending people through courses and expecting behavior to change on its own doesn\u2019t work. If they don\u2019t apply what they learned within a week, most of it disappears.<\/p>\n

The minimum learning path includes these three courses from Anthropic:<\/p>\n