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When I joined a prior organization as CMO, GenAI wasn't being leveraged at all. With the rising wave of AI adoption across the marketing and tech sectors, I needed to find a meaningful way to introduce AI tools to the entire team.

But with any new technology, the problem was about making adoption matter. I didn't want a series of AI experiments done in siloes. I wanted something more substantial — a new operational model, an AI-friendly culture, and a rallying cry rolled into one.

Even before the board had asked for an AI strategy, I suspected that call was coming. So I decided marketing would lead the way with an initiative that I called "Project 30."

The Launch of Project 30

The idea was simple: every leader on my team had a plan to drive a 30% improvement in pipeline, cost, or efficiency using generative AI, which they would report on every quarter. We called it Project 30, and it became our early framework for measurable, accountable progress.

We rolled it out in three phases:

• Phase 1: Make the most of our existing tools. Early on, we focused on using the tools we already had, setting clear targets, and tracking results.
• Phase 2: "Drink our own champagne." As a martech company, our marketers had already been working closely with our product team to design in-house AI capabilities for our product. This phase was about becoming experts in these new AI-powered features by being the first customers.
• Phase 3: Build a target headcount that included AI agents. The final step was to redesign our team as a "humans + agents" organization, with an initial goal of 80 humans (our existing headcount) and 40 AI agents working together.

Looking back to a year ago, those numbers would likely be even higher today. Project 30 was an excellent jumping-off point for AI adoption, but I quickly realized that it alone wasn't enough. Creating AI workflows or one-off use cases was not the long-term solution we were looking for. To truly unlock our potential, we had to go beyond treating AI as something to tack on to our existing process. Instead, we needed to nest it into every layer of our marketing operations.

Introducing: The Nesting AI Framework

Using the lessons from Project 30, I developed what I call the "Nesting AI" approach. It's a framework designed to weave AI through every layer of the organization, rather than managing it as a separate function.

In this model, there are six "nested" layers, sandwiched between business alignment at the top with culture at the foundation:

1. Business Alignment

Everything has to tie back to the business, especially a team-wide AI-powered transformation. Before any tools or workflows, I asked: what outcomes matter to the company?

When we launched Project 30, there was no mandate from the board asking us to invest in AI tools. But by the time they started asking for monthly updates, our department was already building. The CEO's Chief of Staff even titled the marketing section of those monthly reports with "marketing is leading the way on AI adoption."

Company-wide, we formed an AI council with representatives from each department responsible for company-wide alignment and AI governance. Externally, we launched an AI Innovation Lab just for marketers, inviting team members to share challenges we could help solve — and potentially incorporate into our products.

As CMO, I knew my role extended beyond optimizing marketing. It also included evangelizing AI across the company, using our department's learnings to jumpstart the rest of the team.

2. Marketing Strategy & Plans

Embedding AI into our marketing strategy required weaving it into a regular part of our planning rhythms, from top to bottom.

Every leader had ownership of a plan tied to Project 30, included as part of their quarterly business review: goals, progress, feedback, and lessons learned. This included a standardized template that tied each team's plans to agreed-upon KPIs.

Company-wide, we talked about AI in every all-hands meeting. This helped us emphasize that AI adoption was core to the business rather than an experiment spearheaded by the marketing department.

3. Team

Meaningful AI transformation is as much about people as it is about technology. I believe CMOs have to lead from the front with a hands-on approach. By experimenting personally and transparently, leaders can encourage more confidence across the team.

I brought in outside experts like Liza Adams to help kick off enablement. We converted our regular marketing learning sessions into AI enablement sessions and created a "perfect day" model for each role, showing how every marketer on every level of the org should carve out time for AI learning during the workday. We made sure to emphasize that this was never meant to be a weekend job.

We visualized our future org charts — one that outlined how AI agents interacted with humans — to make clear that AI would enhance, not replace, our team.

4. Operating Cadence

To truly embed AI, it had to show up everywhere in our operating cadence. Below are just a few of the ways we wove it in regularly:

• Weekly: I sent a "Top of Mind" note to the marketing team, always including an AI mention, like a helpful resource or a shout-out for a teammate's success story.
• Biweekly: Our extended marketing leadership meetings had a standing AI agenda item.
• Monthly: We shared Project 30 results in a monthly board report and celebrated wins in marketing and all-hands meetings.
• Quarterly: Every leader presented AI outcomes in their QBRs focused on Project 30.

As a team of marketers, it was easy for us to steel-thread AI into all of our internal processes.

5. Execution & Workflows

Normally, this is the area where most AI conversations begin, but it's also where many stop. For us, execution and workflows were a part of every phase of Project 30.

We started with goals tied to efficiency that would enhance our sales motion, like creating battlecard materials 75% faster.

In conversation with other CMOs, I noticed we all shared the same pattern: early success in efficiency-focused use cases, followed by expansion into revenue-driving work once teams gained confidence.

6. Culture

Culture is both the foundation and the capstone of the Nesting AI model. I intentionally place it opposite business alignment in the "sandwich," because every layer in this framework is deeply connected to both.

When we launched Project 30, I noticed two kinds of fear on the team: "Will AI replace me?" and "What if I can't learn this?"

We addressed both fears head-on. The visual org charts showing humans and agents side by side reinforced that we were designing for augmentation, not replacement.

To tackle the fear of falling behind the learning curve, we had our "perfect days" for marketers that included designated time for AI education as part of the job itself. Learning was built into calendars, supported by no-meeting Fridays twice a month, and reinforced through regular peer recognition.

Why do we emphasize culture with AI adoption? Because this shift is a deeply human process. The more people understand AI's role in elevating their work, the more ownership they take. I've seen team members land new roles because of the skills they built during this journey. That's incredibly rewarding as a leader.

From Add-On to Operating Model

Where Project 30 gave us an initial goal to drive towards, Nesting AI gave us a framework we could lean on long-term.

With this model, AI became a truly integrated part of how we worked: how we planned, how we met, how we learned, and how we measured success. The ultimate goal isn't to have "AI projects." It's to build an organization that thinks, plans, and grows with AI woven into its DNA.

Like every new tool, adoption begins with experimentation, but the onus is on leadership to tactfully weave AI into every layer of the goals, operations, and culture of a team. Eventually, AI will feel less like a tool and more like an integral member of the team.