Just a few years ago, nearly every marketer was asking whether or not they should use AI. That question has largely been answered.
81% of marketers use AI in some capacity, and nearly two-thirds say they used it significantly more in 2025 than the year before, according to Brafton’s second annual State of AI Adoption in Marketing Teams report.
As AI becomes a bigger part of marketers’ day-to-day work, organizations are naturally beginning to ask new questions:
- How do we use AI consistently across the team?
- What processes should we standardize?
- How do we maintain quality as AI use grows?
Just as organizations mature their SEO programs, analytics capabilities or content operations over time, AI adoption tends to follow a similar path. Teams gradually build new habits, processes and shared knowledge as AI becomes more integrated into their work.
If AI adoption tells us whether an organization uses AI, AI maturity describes how AI becomes embedded into the marketing function. The AI Content Maturity Curve is one way to think about that evolution.
Stage 1: AI Curious
Organizations need to start somewhere. At this stage, AI is still something marketers are getting to know. A few people may be experimenting with different tools, testing prompts or exploring where AI might fit into their day-to-day work.
Typically:
- AI use is informal.
- Individuals are experimenting independently.
- Different people use different tools.
- Teams are still learning what AI does well.
The focus is mostly on exploration rather than efficiency.
Our survey suggests some organizations are still in this phase. Among marketers whose companies weren’t using AI in 2025, concerns around ethics, output quality, privacy and tool fit were among the most common reasons for not adopting AI.
Like any new technology, curiosity always comes first. Once an organization decides to adopt it, confidence builds gradually over time.
Stage 2: AI-Assisted
Once marketers become more comfortable with AI, it often begins to support individual tasks throughout the workday. This is where many marketing teams are today.
Our survey found marketers most commonly use AI for:
- Headlines and metadata (73%).
- Research and planning (69%).
- Email and social copy (69%).
- Outlining (65%).
- Brainstorming (57%).
- Website copy (57%).
These particular use cases are focused on helping individual marketers work faster or overcome common bottlenecks, like brainstorming, drafting and organizing ideas. In many ways, this is where AI begins delivering productivity gains without major organizational change.
Stage 3: AI Integrated
As AI becomes a regular part of content creation, many organizations start looking beyond individual use cases, moving from “How can I use AI?” to “How can our team use AI more consistently?”
This is where shared resources emerge, such as:
- Standardized content briefs.
- Brand knowledge documents.
- Shared prompt libraries.
- AI-assisted editorial workflows.
- Repeatable review processes.
Interestingly, our survey suggests many organizations are still making this transition.
While 55% of respondents use AI for content creation, only 18% reported using AI for automation, suggesting AI is still primarily supporting individual tasks rather than powering repeatable marketing systems.
That’s not necessarily a weakness, but rather a sign that many organizations are still discovering where AI creates the most value across broader marketing operations.
Stage 4: AI Governed
As AI becomes even more embedded in everyday work, organizations naturally begin thinking about consistency, quality and risk. Questions that may not have mattered during early experimentation become much more important at scale.
For example:
- Which AI tools should employees use?
- What information can safely be shared with AI?
- Which tasks require human review?
- How should individuals or teams handle AI-generated content before publication?
Our survey reflects this.
While AI adoption is widespread, 61% of marketers say they’re still learning AI as they go, and 58% report their organization doesn’t yet have a formal AI policy.
That’s understandable, since organizations likely only develop governance after a technology becomes valuable enough to truly standardize.
Among companies that have implemented AI policies, most focus on practical guidance — governing approved tools, acceptable use cases, data inputs and expectations for human review.
Whether “good governance” means regulating AI adoption or helping an organization use AI more confidently and consistently depends almost completely on the industry.
Stage 5: AI Optimized
For some organizations, AI gradually becomes part of the marketing infrastructure. Rather than supporting isolated tasks, it begins supporting repeatable processes across the entire content lifecycle.
That often means investing in assets that make AI more useful over time, such as:
- Brand knowledge bases.
- Customer research libraries.
- Messaging frameworks.
- Case study repositories.
- Standard operating procedures.
- Workflow documentation.
At this stage, organizations recognize that AI improves when the information around it improves.
Instead of relying on the same generic knowledge every model has access to, they’re giving AI access to their own internal expertise, experiences and institutional knowledge.
By this stage, the benefit of AI has moved far beyond “faster content” to content that’s more informed, consistent and reflective of the organization using it.
Is There a ‘Finish Line’ for AI Maturity?
Organizations don’t move through AI maturity stages on a schedule, nor should they. Company size, industry, resources and business priorities all influence how AI adoption evolves for any given organization. Even within the same company, different teams may find themselves at different stages.
This framework is simply a way to think about how AI becomes more deeply embedded over time.
Ask yourself:
- Are our AI workflows documented or mostly improvised?
- Is AI platform knowledge shared across the team or tied to individual marketers?
- Have we created reusable systems, or are we starting from scratch each time?
- Is AI helping individual contributors, or is it improving how our team operates?
Answers to these questions might help reveal where your organization should naturally move next.
The challenge isn’t gaining access to AI anymore — all the tools marketers could need already exist. It’s building the skills, processes and standards that help organizations turn widespread adoption into long-term value. That’s what AI maturity is really about. Not using AI more, but using it more intentionally.
