From my experience as a CMO and AI consultant, I’ve seen that teams don’t leap from zero to AI-native overnight. Adoption happens in stages, and knowing how to move from one stage to the next can be a real competitive advantage.
Here’s the seven-stage journey I see most often. In each stage, you’ll find what actually happens, who usually drives it, and how you might use AI assistants, dedicated AI marketing tools (like Jasper, Midjourney or Synthesia) or automation & agents.
Stage 1: Curiosity
It often starts with someone playing with ChatGPT or an AI image tool in their spare moments. There’s excitement, yes—but also scepticism. Some wonder, is this going to help me—or replace me? For now, AI is side-interest, not part of how campaigns work.
People experiment on their own time with ChatGPT or image tools.
Excitement sits uneasily next to scepticism: “This is cool… but is it useful?”
No connection to actual campaigns or systems yet.
Driver: team individuals curious to explore—not coming from the top.
Stage 2: Ad-hoc use
Curiosity starts to slip into meetings. “AI champions” use ChatGPT for blog outlines, email subject lines, or quick brainstorming. The results vary—sometimes brilliant, sometimes far from it. But the mood shifts: “Hey, that saved me 20 minutes.” Sharing is informal—“check this out” in Slack or a quick desk-side demo.
People test AI for brainstorming, rough copy, subject lines.
Results are mixed—prompting skills are basic.
Still separate from formal workflows.
Driver: team members who champion AI and share small wins informally.
Stage 3: Structured pilots
This is where things get interesting. The team agrees to test AI against specific tasks—content repurposing, ad copy, blog outlines, subject lines—with intention. They build a shared prompt library and start training one another. They trial marketing-specific tools (Jasper for copy, MidJourney for visuals) and the AI already in familiar platforms (HubSpot’s content assistant, Canva’s Magic Write, Notion AI).
Clear use cases: repurposing, ad copy, blog outlines, subject lines.
Shared prompt/use-case libraries are in place and updated.
Testing with marketing-specific AI tools and embedded AI in your own platforms.
Training to build confidence and consistency.
Driver: usually the CMO, marketing director or head of performance; they formalise the pilots, set basic guardrails and gather insights.
Stage 4: Custom AI projects
Once pilots feel useful, the team wants more. They move from generic prompts to building their own—custom GPTs trained on brand tone or messaging, campaign-specific assistants, or even agents that monitor performance and recommend tweaks. These usually run with a small team—experimenting, not yet part of everyday operations.
Custom GPTs or agents tailored to brand workflows.
Examples: tone-of-voice GPT, campaign launch assistant, performance-monitoring agent.
Run by small, focused project teams.
Driver: innovation or AI champions, often with CMO sponsorship.
Stage 5: Workflow automation
AI starts to feel like part of the engine. Reporting, segmentation, campaign generation—some of it becomes automated. Zapier, Make or n8n glue together HubSpot, Google Ads, creative tools and analytics. You now juggle both dedicated tools (like Jasper, Synthesia) and built-in AI (like HubSpot, Meta’s creative tools).
AI built into daily operations and workflows.
Automation via Zapier, Make, n8n connecting tools.
Campaign creation, reporting, segmentation start to run without manual effort.
Driver: marketing operations or CMO driving automation; supported by performance and CRM teams.
Stage 6: Scaled adoption
AI is now standard—not experimental. Every sub-team (creative, performance, CRM, analytics) taps it daily, under human oversight. Governance is formal—playbooks, brand safety, training. And AI dives deeper—optimising retention, powering lifecycle flows, predictive modelling and personalised messaging in tools like HubSpot or Klaviyo.
Daily use across creative, CRM, performance and analytics.
Human-in-the-loop safeguards quality and brand compliance.
Playbooks, governance, training become common.
AI powers retention, personalisation, audience modelling.
Driver: solid top-down commitment from CMO or CEO, with department leads making sure it sticks.
Stage 7: AI-native marketing
At last, AI is invisible. It’s not a separate tool—it’s how the work gets done. Custom models, agents, automations span the marketing stack. You’re focused on strategy, insights and creativity; AI handles execution and scale—auto-triggered nurture journeys, AI-created creative, agents optimising performance continuously.
AI as the default operating mode.
Fully custom, integrated AI across the stack.
Focus shifts to strategy and insights; AI handles execution.
Driver: board or CEO level mandate ensures AI is embedded in culture, systems and strategic direction.
How to transition between stages
Climbing the adoption curve isn’t about budgets or big-bang investments. It’s about small, deliberate steps. Here are actions a CMO or marketing leader can take at each stage to move forward:
Stage 1: Curiosity
Encourage the team to share experiments in a dedicated Slack/Teams channel.
Run a 30-minute lunch & learn where someone demos their favourite AI use.
Stage 2: Ad-hoc use
Pick two safe, repeatable use cases (subject lines, blog outlines) and define them as pilots.
Create a shared prompt library in Notion or Google Docs.
Stage 3: Structured pilots
Nominate an “AI champion” to test a custom GPT for brand tone or campaign copy.
Run a small cross-functional project (e.g. AI-supported campaign launch) and capture outcomes.
Stage 4: Custom AI projects
Map one routine process (like weekly reporting) and test an automation using free Zapier or Make.
Pilot AI features already in your current tools (HubSpot’s assistant, GA4 insights, Notion AI).
Stage 5: Workflow automation
Draft a lightweight AI playbook with prompts, examples and workflows.
Run short internal training sessions to upskill sub-teams.
Stage 6: Scaled adoption
Audit your stack to spot opportunities for bespoke models or agents.
Set an executive-level commitment that AI is not optional but part of strategy.
Stage 7: AI-native marketing
Build AI into your culture—make experimentation and upskilling part of ongoing practice.
Explore where to extend AI beyond marketing (sales, product, customer success) to embed it organisation-wide.
Your Marketing AI Adoption Checklist
AI adoption is a journey, not a race. Most marketing teams sit somewhere in the middle — testing pilots or running custom projects. What matters is knowing your stage and the two small steps that will move you forward.
To make this easier, I’ve created a Marketing AI Adoption Checklist. It breaks down the seven stages into a simple tick-box format, with two free actions at every stage to help you progress.
🔗 Download the Marketing AI Adoption Checklist here
And if you’d like more support in guiding your team up the curve, I help marketing leaders navigate this journey with clarity and impact. Let’s talk.
