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Most marketing and sales teams know AI has huge potential. The question is where to start. This article helps you understand where your team stands today and which concrete steps will get you to the next level.
MIT Sloan found that companies with advanced AI capabilities crush their competition by 20% in revenue growth. Yet, most marketing teams are still in the early stages of AI adoption (Jasper).
That creates a massive opportunity. While your competitors hesitate, you can build AI workflows that improve content quality, productivity and team workload.
We've mapped five distinct stages that every team goes through on their AI journey. Where do you land?
An AI maturity model assesses how well your team uses AI. Each level has specific skills, tools, and capabilities. These frameworks help you spot gaps in your current approach and plan your next moves.
AI maturity directly impacts your marketing results and team efficiency.
Teams at higher maturity stages complete campaigns faster, generate better-quality content, and free up time for creative and strategic work.
Early-stage teams often waste resources on disconnected AI experiments. They purchase tools without clear use cases, or before sorting out the existing processes. They struggle to measure impact or scale successful pilots. After all, 95% of all AI fail. (MIT: State of AI in business, 2025)
Advanced teams treat AI as a core capability, not a nice-to-have add-on. This mindset shift creates benefits over time.
We all started here. For decades, this was the only way marketing worked. But you can still find teams that do everything by hand. Competitor analysis takes days of heated research. The content writer crafts each social media post individually, thinking of every word choice.
AI is not on the agenda. In fact, it is viewed with deep suspicion and worry. "Nothing beats human creativity," the CMO declares. The team takes pride in their craftsmanship.
Teams at this stage produce beautiful work. Their campaigns have personality and authentic brand voice. But scaling up feels impossible without hiring an army of people. They watch competitors with less employees launch campaigns faster and wonder how they manage it.
The team finally starts talking about AI in meetings. "Maybe we should try that ChatGPT thing everyone's using", someone suggests during the weekly planning session. But when they approach IT about access, the response is swift and predictable: "Security risk. Absolutely not."
Microsoft Copilot gets the green light instead. It plays nice with their Office setup, so IT approves. The team gets excited about having an "AI assistant," but reality proves disappointing.
A few people use it to rewrite emails or brainstorm subject lines during quiet moments. Results vary wildly based on individual prompt skills. Most remain sceptical or unaware of all that AI can offer.
Six months pass with no formal training or shared learning. The team has no idea which AI experiments actually improved productivity or quality.
Everything changes when IT finally opens the floodgates. The team gains access to ChatGPT, Claude, Gemini, and other top AI tools. The focus shifts from "can we use AI?" to "how should we use AI?" Suddenly, the world of AI possibilities explodes beyond the limited Copilot experience they've known.
The breakthrough comes when they stop thinking about simple tasks and start optimizing larger processes. Instead of just using AI to only grammar check blog posts, they use different tools for research, outlining, drafting, headline generation, and SEO optimization. Content production triples without sacrificing quality.
The team starts creating a shared AI culture. Prompt libraries emerge for common tasks like writing product descriptions and generating ad copy.
By the end of this stage, the team develops genuine AI expertise. They understand prompt engineering principles, know which models excel at specific tasks, and can troubleshoot when outputs miss the mark.
But they're still manually copying and pasting between platforms, dreaming of the day when these workflows connect automatically.
The team finally figures out how to build automated workflows that connect multiple AI tools with their existing marketing stack. Connectors, MCPs and automation tools like Zapier, Make.com, n8n, Workato become their best friends.
Everything clicks when AI integration becomes systematic rather than sporadic. Now most marketing work becomes automated or has AI-enhanced steps.
The culture also evolves. Teams create dedicated spaces for sharing learnings and best practices. They actively monitor how well AI implementations perform and adjust accordingly.
The most advanced marketing teams are currently in this stage. There aren’t a lot of them.
While no marketing team has reached this stage, let’s picture what it would look like.
AI powers all marketing activities in some way. Budgets, roles and structures are redesigned around AI capabilities. Marketing processes assume AI involvement from day one.
Every team member knows how to build custom AI agents. AI agents can execute tasks autonomously and independently. Marketers design systems rather than only carrying out tasks.
Smart organizations prepare for risks through formal governance. Human oversight stays mandatory for major decisions. Clear ground rules and comprehensive training prevent AI risks.
While AI agents exist, running agents for marketing tasks is still slow and expensive, making automation or manual work a better option. Moreover, most teams don’t yet have the competence to build or maintain robust agents.
Start by evaluating your team across these four dimensions:
Moving up the maturity ladder requires more than good intentions:
Most marketing teams waste months testing random AI tools without a plan. They buy subscriptions nobody uses, run pilots that never scale, and wonder why AI hasn't transformed their work.
A structured maturity roadmap changes that. It shows you where you are, what capabilities you're missing, and which investments will pay off.
Start by assessing your current stage using the four dimensions above. Be honest about gaps in your strategy, data, technology, and people. Then pick one stage higher as your target and build a 90-day plan to get there.
The teams winning with AI right now aren't the ones with the biggest budgets or the fanciest tools. They're the ones who build capabilities, learn from experiments, and create systems that scale.
Your competitors are already moving. The question isn't whether to build AI maturity, but how fast you can do it.
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