AI is changing the game for marketing teams across the globe. But not everyone is getting it right. So, what sets the successful teams apart?

After working with hundreds of marketers, leaders, and teams, here’s 7 common things smart teams are doing to nail their AI marketing adoption:

AI is changing the game for marketing teams across the globe. But not everyone is getting it right. So, what sets the successful teams apart?

1. Strategy First: They Know AI Isn’t a Silver Bullet

They understand that AI isn’t going to fix rubbish marketing.

If you don’t have a solid marketing strategy in place, AI will just amplify your problems. Before bringing in AI, high-performing teams make sure they have the fundamentals covered—clear goals, customer insights, data-driven strategies and strong messaging.

AI exists to scale and optimise what’s already working; it can’t fix a weak foundation.

Why this works:

• AI amplifies what you already have—so with a strong strategy, AI can enhance and scale your marketing efforts.
• AI is used to build on success, not fix existing problems.
• If your team needs help getting their strategy nutted down check out our Marketing Growth Academy 

2. They See AI as a Full Team Effort

AI knowledge and strategy doesn’t sit in silos. Leadership may set the vision, but they open the floor to input from everyone.

One of the ways they do this is through AI planning days—dedicated sessions where they educate and inspire the whole team and crowdsource brilliant new ideas and innovative uses of AI. 

By involving everyone, from different marketing functions and teams, they get the full picture on how AI can solve specific challenges and where it adds the most value.

They also encourage a “test and learn” culture, understanding that AI implementation isn’t perfect from day one. They run competitions and challenges to find possible use cases—knowing that some things will work while others won’t, and that’s OK.

Why this works:

• Buy-in from the whole team ensures AI adoption happens faster and with less resistance.
• Transparency means people feel less scared about “AI taking my job”
• A team effort generates more creative, cross-functional ideas for AI use cases that wouldn’t surface otherwise.

3. They Have a Clear, Simple AI Vision and Roadmap

They’re not just looking for shiny use cases—they have a clear roadmap. 

This isn’t some complicated, jargon-filled document either; it’s a simple one-pager focused on the next 12-18 months (who knows where we’ll be beyond that!) showing where AI will add value, what the goals are, and how it aligns with the overall marketing strategy.

Everyone can see how their work fits into the AI plan, and the roadmap provides a clear path forward, ensuring everyone is clear on the what, when, how, and why of AI.

Why this works:

• A clear roadmap ensures alignment between departments, making AI adoption smoother.
• It helps secure buy-in from leadership by clearly showing the business value AI can deliver.

4. They Build (and Train) a Team of AI Champions

Successful marketing teams don’t make AI one person’s job. Instead, they create an AI squad—a group of cross-functional SMEs tasked with identifying the best AI use cases, experimenting with tools, and keeping the momentum going.

These champions aren’t just marketing focussed either —they work cross-functionally with  Compliance, Sales, IT, HR, Finance and Legal so that AI adoption doesn’t get stuck in silos (or blocked further down the track!).

Why this works:

• You can break down silos and get better, more integrated AI adoption across teams.
• AI champions act as internal advocates, helping sustain long-term AI initiatives and momentum.

5. They Focus on Solving Problems, Not Chasing Tools

One of the biggest mistakes we see is focusing on the tech first. The smartest marketing teams flip this on its head by starting with the problems they want to solve.

Instead of adopting the latest AI tool and then wondering how on earth they’ll get value out of it, they ask questions like:

• Where are we losing time or efficiency?
• Where are our customers experiencing friction?
• Where could we drive better results or performance?
• Is AI the answer? How can AI make this easier?

By being problem-centric, they ensure that AI initiatives deliver real, measurable value. AI is seen as a tool to solve bottlenecks and inefficiencies, not just a shiny object to show off.

Why this works:

• Focusing on problems ensures AI investments are tied to actual business needs.
• It delivers measurable ROI, as AI is used to solve clear, existing pain points.

6. They Create Clear AI Guardrails for Innovation

The best teams balance freedom with responsibility. They create clear AI guardrails—policies that outline what AI can and can’t be used for, ensuring experimentation happens safely and ethically.

These guardrails aren’t about restricting innovation; they’re about empowering teams to try new things while keeping within the boundaries of data privacy, security, and compliance.

By doing this, they build trust both internally and externally, ensuring that AI initiatives are not only creative but also responsible.

Why this works:

• Guardrails allow for safe experimentation without sacrificing creativity.
• It ensures that AI initiatives remain compliant with industry regulations and ethical standards.
  

   

7. They Invest in AI Literacy and Upskilling

AI can seem daunting, especially for teams who worry about job security or feel like they lack the skills to adapt. That’s why successful teams invest in ongoing AI literacy. 

They bring in marketing AI experts (like us!) to run learning days, facilitate strategic sessions, and provide continuous upskilling to make sure their teams can confidently embrace AI.

It’s not just about giving people new tools—it’s about changing mindsets and giving them the skills to make AI work for them. These teams make sure that everyone, from leadership to practitioners, understands how AI fits into their day-to-day work.

Why this works:

• AI literacy reduces fear and anxiety, making teams more open to experimentation.
• Upskilled teams can innovate and come up with better AI use cases, ensuring a higher return on AI investments.

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