Here's the 7-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 or Clay) 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.

What happens:

• 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.

Skills needed:

• Basic familiarity with ChatGPT or Claude

• Understanding of AI image tools like Gemini 

Use case examples:

• Generating email subject line variations for A/B testing

• Creating blog post outlines from campaign ideas

• Testing image generators like Gemini Nano Banana for creative inspiration

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.

What happens:

• People test AI for brainstorming, rough copy and subject lines.

• Results are mixed as prompting skills are basic.

• Still separate from formal workflows.

Skills needed:

• Basic prompting techniques

• Understanding prompt structure

• Ability to iterate on AI outputs

Use case examples:

• Drafting email subject lines and testing variations

• Creating blog post outlines and first drafts

• Brainstorming campaign headlines and concepts

Driver: team members who champion AI and share small wins informally.

Reality check: Most marketing teams are at stage 1 or 2. If this is you, you're not alone but there's an enormous competitive advantage in being deliberate about moving forward.

Stage 3: Structured pilots

The team agrees to test AI against specific tasks: content repurposing, ad copy, blog outlines, subject lines with intention. They build a shared use case library and start training one another. They trial marketing-specific tools and the AI already in familiar platforms (HubSpot's content assistant, Canva's Magic Write, Notion AI).

What happens:

• 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.

Skills needed:

• Advanced prompting and prompt engineering

• Building and maintaining an AI use case library

• Using ChatGPT or Claude

Use case examples:

• Repurposing blog posts into LinkedIn posts, email newsletters, and social content

• Creating multiple ad copy variations for A/B testing across Meta and Google

• Generating email subject line variations with consistent brand tone

Driver: usually the CMO, Marketing Director or Head of Growth; they formalise the pilots, set basic guardrails and gather insights.

Ask yourself: Do you have an AI pilot with KPIs and documentation? If not, you're still experimenting, not piloting. Structure matters.

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 agents that monitor performance. These usually run with a small team: experimenting, not yet part of everyday operations.

What happens:

• Custom GPTs or Claude Skills agents tailored to brand workflows.

• Run by small, focused project teams.

Skills needed:

• Building Custom GPTs 

• Creating Claude Projects and Skills

• Developing AI roadmaps and project plans

• Cross-functional collaboration for AI projects

Use case examples:

• Custom GPT trained on brand guidelines and tone of voice for consistent copy creation

• Campaign-specific Claude Project loaded with brand assets, messaging, and audience insights for product launches

• Custom GPT trained on customer research and consumer insights to inform messaging strategy

Driver: innovation or AI champions, often with CMO sponsorship.

Reality check: Even using a Custom GPT is rare. If your team is building these, you're ahead of the curve.

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. 

What happens:

• AI built into daily operations and workflows.

• Automation via Zapier, Make, n8n connecting tools.

• Campaign creation, reporting, segmentation start to run without manual effort.

Skills needed:

• Creating bespoke AI agents and workflows

• Building automations with Zapier, Make, or n8n

• Connecting multiple platforms via APIs

• Understanding webhook and trigger-based automation

Use case examples:

• Automated weekly performance reports compiled from Google Ads, Meta, and GA4 using n8n or Make

• Content workflows that repurpose new blog posts into social, email, and ads automatically

• Automated customer segmentation in HubSpot based on behaviour and engagement data

Driver: CMO driving AI adoption; supported by Marketing Operations, Performance and CRM teams.

Reality check: Very few companies get to automation. The ones that do? They often use AI in their products too. AI is not just a marketing initiative, it's organisation-wide.

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: policy, governance, playbooks, brand safety, training. And AI dives deeper: optimising retention, powering lifecycle flows, predictive modelling and personalised messaging.

What happens:

• 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.

Skills needed:

• AI governance and compliance frameworks

• Building and maintaining AI playbooks

• Training programmes for team-wide AI adoption

• Advanced analytics and predictive modelling

• Cross-functional AI project management

Use case examples:

• Lifecycle marketing flows in Klaviyo that adapt messaging based on customer behaviour patterns

• Content System that generates assets automatically 

• AI Voice Agent that calls customer to retain them 

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.

What happens:

• AI as the default operating mode.

• Fully custom, integrated AI across the stack.

• Focus shifts to strategy and insights; AI handles execution.

Skills needed:

• Working with custom AI models and fine-tuned systems

• Building and managing complex agent ecosystems

• Strategic AI architecture across the marketing stack

• AI ethics and responsible AI implementation

• Focus on creativity, customer insight, and strategic thinking

Use case examples:

• Fully automated campaign orchestration from ideation to execution and optimisation across all channels

• AI agents that continuously test, learn, and optimise creative and targeting without manual intervention

Driver: board or CEO level mandate ensures AI is embedded in culture, systems and strategic direction.

Reality check: The future is coming… but it's not here yet for most teams. Stage 7 requires deep organisational commitment and infrastructure most companies are still building toward.

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:

From 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.

From Stage 2: Ad-hoc use

• Pick 2 safe, repeatable use cases (subject lines, blog outlines) and define them as pilots.

• Create a shared Use Case library in Notion or Google Docs.

From 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.

From 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.

From Stage 5: Workflow automation

• Draft a lightweight AI playbook with prompts, examples and workflows.

• Run short internal training sessions to upskill sub-teams.

From 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.

From 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 AI Adoption curve, I help marketing leaders navigate this journey with clarity and impact. Let's talk.