In most marketing orgs, AI is still treated like a shiny intern. It’s tasked with execution: write the blog post, generate the visuals, draft the subject lines. But the hard stuff? The high-leverage thinking? The role of AI in driving real, measurable growth? That’s still sitting in a Google Doc somewhere, half-baked.

The truth is, AI won’t transform your marketing team just because you added ChatGPT to your tech stack. It will when you treat it like a teammate. A strategist. A multiplier of your best people and ideas.

In this Mavuus Coffee Chat, Fab Dolan (Founder of 99 Ravens AI, ex-Google), Brett Willms (Co-founder of Laetro, former CMO at Gusto), Liza Adams (AI transformation advisor and GTM strategist), and Justin Parnell (VP of Marketing, GPT trainer, and workflow architect) shared the practical frameworks, use cases, and KPIs that are helping CMOs move beyond experimentation and integrate AI into the fabric of their teams.

Table of Contents:

1. Why Most CMOs Still Don’t Have an AI Strategy
2. The Shift from Tools to Teammates: Rethinking AI
3. How to Build an AI Strategy That Delivers
4. The Metrics That Matter: Measuring AI Impact
5. The AI Tools Top CMOs Use

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1. Why Most CMOs Still Don’t Have an AI Strategy

AI is everywhere in marketing. Content is being generated in seconds. Research is being delegated to AI agents. New tools launch daily, promising to unlock creativity, productivity, and growth.

And yet, only 10% of CMOs feel they’ve fully implemented an AI strategy, even as 94% say AI has positively impacted their business. This disconnect reveals an important truth: most CMOs don’t lack interest in AI. They’re lacking integration.

During our Mavuus Coffee Chat, we asked participants how they leverage AI in their marketing work. The results revealed that content generation leads by a significant margin, followed by strategy development. Use cases like data analysis and resource augmentation lag far behind.

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These results didn’t surprise Fab Dolan, founder of 99 Ravens AI and former marketing leader at Android. “Most AI tools have been built from the task up. They help with execution—writing, designing, and editing. But the biggest failures in marketing usually happen at the strategic level,” said Dolan.

Brett Willms, former CMO of Gusto and co-founder of Laetro, agreed and pointed to a shift already underway. He noted that, “Strategy is coming up fast. It used to be that AI couldn’t really do strategy. Now, it absolutely can. We’re building agents internally that can run full brand frameworks—do the research, ask the right questions, and generate insights.”

There’s no question that content automation unlocked the first wave of excitement. However, CMOs are starting to realize that better creative output begins with a stronger, AI-assisted strategy. AI is evolving into a thought partner that can enhance strategic thinking, accelerate research, and turn internal expertise into scalable systems.

Liza Adams, a marketing and AI advisor who works with executive teams on AI adoption, put it this way: “I don’t treat AI like a tool. I treat it like a teammate.” And that’s where the transformation begins. When CMOs move beyond one-off tasks and start integrating AI into how they think, plan, and lead—that’s when it becomes a strategy.

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2. The Shift from Tools to Teammates: Rethinking AI

For many marketing leaders, the first wave of AI adoption felt tactical—content generation, image creation, summarizing research. These use cases brought speed, but not necessarily strategic depth.

Now, a second wave is emerging.

Instead of viewing AI as a faster way to write blogs or create ad copy, marketing leaders are starting to treat AI as a thought partner. One that can push strategic thinking, challenge assumptions, and expand what’s possible for lean teams.

At the heart of this shift are a few emerging use cases that go far beyond task automation:

AI as a Strategic Collaborator

AI is becoming a helpful partner in shaping ideas, not just executing them:

• Ideation: Models like Claude or Gemini Pro are used in brainstorming sessions to surface angles or questions marketers may not have considered.
• Research synthesis: Perplexity and other agents are now profoundly effective at competitive research, customer insights, and industry scans.
• Framework execution: Custom agents can now run a brand framework or marketing brief end-to-end, including evaluating inputs and suggesting next steps.
  

As Brett Willms shared, “AI now asks curious questions about your strategy framework. It can look at your process, understand it, and run it—fast. You still need to know strategy, but it’s incredible where it’s going.”

Digital Twins: Scaling Human Expertise

Perhaps the most powerful concept introduced was the idea of creating “digital twins.” These are AI agents trained to think like your best strategist or marketer.

Liza shared that she’s built a custom GPT version of herself and trained on her thinking, writing style, past decisions, and strategic preferences. She doesn’t use it to do the work, but to challenge her thinking and uncover blind spots.

Fab shared how one agency replicated the mind of their Chief Creative Officer, turning years of creative intuition into scalable, on-demand strategic input. This kind of augmentation makes strategy more consistent, scalable, and teachable, especially across distributed or fractional teams.

Here’s the key distinction CMOs are beginning to make:

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When AI is treated as a teammate, it becomes embedded in your team's work, not just what they produce. And that opens the door to the next generation of marketing leadership: one that doesn’t just use AI but thinks with it.

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3. How to Build an AI Strategy That Delivers

AI can generate content in seconds. It can analyze data at scale. It can even ideate alongside your team. However, none of that matters if your AI implementation is disconnected from your strategy.

So, how do you move from scattered experimentation to integrated, measurable outcomes?

During our Coffee Chat, the panel outlined a practical framework for CMOs ready to move beyond experimentation and embed AI into the core of their marketing organization.

Step 1: Map Your Team’s “Jobs to Be Done”

The first step is about visibility. Justin Parnell emphasized the importance of understanding where value is already being created inside your team, and which of those functions are ripe for AI integration.

“Start by documenting all the jobs to be done on your team. Look at workflows that already drive impact, and then identify where AI can enhance speed or consistency,” he shares.

That could mean:

• Content production workflows
• Lead scoring and qualification
• Campaign reporting
• Research and brief development

Once those workflows are mapped, Justin recommends baselining KPIs before introducing AI. That way, you can measure whether your AI strategy delivers the lift you expect.

Track KPIs like:

• Time to content completion
• Volume of content produced
• Campaign performance (CTR, conversion rate)
• Lead quality and velocity

It’s impossible to tie AI investment back to business outcomes without this foundation.

Step 2: Codify Expertise

AI can’t scale what you haven’t yet defined. That’s why the next step is turning your team’s best thinking—your strategic frameworks, mental models, and domain knowledge—into something repeatable and teachable.

Fab Dolan warns CMOs not to treat AI implementation like a standard software rollout. “Don’t think of it as SaaS. It’s not just technology—it’s change management. You’re codifying your team’s expertise into a system that others can use and trust.”

This helps scale what already works, especially in high-impact but high-variance functions like:

• Brand positioning
• Campaign strategy
• Creative concepting
• Market research

Think of this phase as building your internal IP into repeatable systems—ones that can be scaled by AI teammates and made available across your entire team, even in distributed or fractional models.

Step 3: Collaborate Across the Org

The final step is cross-functional alignment once you’ve mapped opportunities and codified expertise. AI strategy doesn’t live in a vacuum. It needs to be aligned with broader technology, data, and change initiatives happening across your company.

Liza Adams shared a practical approach that’s worked for many of her clients:

• Start with tools your team already uses (CRM, CMS, analytics platforms)
• Leverage AI capabilities in your existing MarTech stack before layering in new vendors
• Engage IT and Operations teams early, especially when it comes to privacy, data security, and infrastructure

“Driving adoption is easier when you start with something familiar,” said Liza. “From there, you can build momentum and expand into more specialized tools.”

So, when should you introduce new AI tools?

Only when you’ve identified a clear gap that your current systems can’t address, and when you have the support structure in place to adopt them successfully.

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4. The Metrics That Matter: Measuring AI Impact

It’s tempting to measure AI success in terms of speed. But speed alone doesn’t make for better marketing. Yes, AI can help your team move faster. But what really matters is what improves when you do.

Our speakers stressed the importance of distinguishing between vanity metrics and value metrics, and designing your AI strategy around the latter. “Forget vanity metrics,” said Liza Adams. “You have to tie AI to your strategic initiatives, or it’s just noise.”

Here’s what that looks like in practice:

Use Input and Output Metrics to Track AI ROI

Justin Parnell recommends measuring AI performance with a two-part framework:

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By clearly tying input (e.g., 20 blog posts/week) to output (e.g., 30% increase in organic traffic), you create a direct line of accountability between AI activity and business performance.

Don’t Chase Efficiency. Build for Impact.

It’s easy to get caught in the productivity trap. AI can make it look like your team is doing more than ever before, but more content, more emails, and more campaigns don’t automatically mean better marketing.

As Fab Dolan put it, “Better beats more. If your core KPI becomes content per headcount, you’ve already lost sight of the goal.”

In other words, efficiency is a byproduct, not the goal. The real metric is impact, measured in growth, engagement, and resonance with your audience.

And AI should help amplify those results, not just churn out more of the same.

What CMOs Should Track Now

Here’s a simplified checklist for measuring what matters:

• Speed of execution (compared to pre-AI baselines)
• Uplift in campaign performance (CTR, CPL, conversion)
• Quality scores from internal stakeholders or AI evaluators
• Contribution to strategic initiatives (not just tactical wins)
• Knowledge transfer or scale (via AI agents or digital twins)

When CMOs focus on these kinds of metrics, AI becomes a driver of team performance, organizational learning, and long-term growth.

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5. The AI Tools Top CMOs Actually Use

If the last few years have been about testing AI tools, the next few are about choosing the right ones—and building sustainable systems around them. During our Coffee Chat, we asked each expert what AI tools they could not live without.

Here’s what they shared with us.

Fab Dolan: Creative + Contextual

For Fab, choosing the right tools starts with matching them to the task—creative ideation vs. research vs. strategic input. He leans on a few key systems:

• Claude: Fab’s go-to for creative tasks, especially for early-stage ideation and content generation.
  
• Gemini: Best for structured research, fact-checking, and retrieving detailed information fast.
  
• Digital Twin: Fab uses a replica of a top strategist—a trained AI agent that mimics the thinking style, judgment, and frameworks of a trusted marketing leader. This twin serves as a challenger and sounding board, helping to pressure-test briefs and campaign ideas.

“The strategist I modeled thinks totally differently from me. That’s the point. It helps me see what I’m missing,” shared Fab.

Brett Willms: High-Volume Creative at Scale

Brett’s background in performance marketing and creative operations means his AI toolkit is built for output and collaboration.

• Claude 3.5: The best creative writer on the market, in his view—ideal for marketing copy and ideation.
  
• Gemini Pro: His top pick for interactive collaboration. Brett noted that Gemini “asks better questions” and helps marketers sharpen their thinking.
  
• ComfyUI: A lesser-known but critical tool for visual production. Used internally at Laetro, it enables custom visual workflows, image consistency, and production-quality outputs.

Liza Adams: Thought Partnership and Practicality

Liza’s AI setup is designed around thought partnership. Her goal isn’t to automate—it’s to amplify.

• “Lisa GPT”: A custom-trained AI based on her personal writing style, strategic frameworks, and communication style. It helps her refine her ideas and identify blind spots.
  
• Claude: Her preferred model for high-level strategy and reflection. “Claude thinks like me,” she said, making it ideal for co-creating content and plans.
  
• MarTech-first strategy: Liza advises CMOs to start with the tools they already use. Many CRM, email, and analytics platforms have native AI capabilities. Start there, then layer in more specialized tools only when needed.

Justin Parnell: Workflow First, Tool Second

Justin’s approach to AI is deeply pragmatic and focused on workflow speed and alignment.

• Voice Input: Justin uses voice mode with LLMs like Gemini and Claude to accelerate research and ideation. It’s faster than typing and creates a more natural back-and-forth.
  
• Alignment Agents: Justin emphasizes the importance of agents that not only generate content but also evaluate it. These tools are trained to recognize whether an output aligns with ICPs, brand voice, or strategic intent.
  
• Tool Selection Philosophy: Look for systems that feel like thinking partners, not just content generators. “You want the model to understand your market, not just spit out copy.”

Rather than chasing the newest product, these leaders are building AI systems around how their teams think, create, and collaborate.

And that’s the future of AI in marketing—not just plug-and-play tools, but thoughtful integrations that enhance the human side of strategy and execution.

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Scale Your Thinking, Not Just Your Output

If your current AI “strategy” is asking ChatGPT to rewrite a subject line in five tones, you’re not alone. Most marketing teams are still hovering at the surface: plugging in prompts, tweaking outputs, and hoping no one asks what the ROI is.

But the AI that helps you write faster is not the same AI that helps you think better.

We’re not here to take away your GPT subscription or shame your 47-tab Perplexity addiction. We’re just here to help you turn all that experimentation into something that scales—like an AI agent that understands your ICP, or a digital twin of your top strategist that doesn’t take PTO.

Because prompts don’t build marketing strategy, people do, preferably with a community behind them.

That’s why we built Mavuus—a space for CMOs and senior marketers to share what’s working, trade frameworks, audit each other’s AI playbooks, and build the kind of marketing orgs that don’t just use AI but lead with it.

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Join Mavuus Today!