It’s launch week. You’re a product marketer writing the announcement blog post. The one that needs to nail your brand voice, reflect six months of positioning work, and land differently than every other AI company announcement hitting inboxes that same day. You open a new tab, pull up your AI tool of choice, and start prompting. You include the brief, the audience, the key messages. You paste in a few examples of copy that your team has approved before.
The output comes back polished and confident, but just far enough off-brand to need a full rewrite. So you try again, this time with a better prompt. The second draft is closer. The third is almost there. By the time you have something usable, you’ve spent more time prompting, reviewing, and rewriting than you would have spent just writing it yourself.
Most marketers have been here. And the instinct to refine the prompt and add more detail is a reasonable one. For a while, it was even the right one.
But there’s a ceiling on what better prompts can do—and most marketing teams have already hit it. The missing ingredient isn’t a better ask. It’s context.
The problem with prompt engineering alone
When generative AI first became popular at work, a new skill emerged among knowledge workers: prompt engineering. The better your prompts, the better your outputs. Be specific. Add examples. Define your tone. Explain your audience. Marketing teams began to take online courses, develop prompt libraries, and create internal playbooks to get more out of their tools. For a while, it worked. Getting good at prompting genuinely improved results.
But as AI tools evolved, the gap between capability and context became impossible to ignore. Models got more powerful. Outputs got more polished. And yet the output still came back generic, because no matter how good the model, it was starting every session from zero. It didn’t know your brand. It didn’t know your campaign. That knowledge had to come from you every single time.
The instinct to write better prompts was never wrong. But the most capable AI tools today aren’t winning on better prompt responses. They’re winning by needing fewer prompts in the first place because they already know what you’re working on.
Why context is so important
To understand why context matters, it helps to be specific about what we mean by it. Context isn’t a longer prompt. It exists at two levels:
The first is stored context: the accumulated knowledge that makes work coherent. Your brand voice. Your campaign history. Your audience. Your positioning. The strategic foundation that took months to build. and These make the difference between a draft that sounds like any company and one that sounds unmistakably like yours.
The second is situational context: what’s happening right now. The document you’re working in. The feedback you just got. The messaging framework that the campaign is built around. This is the kind of context that isn’t included in a style guide. It exists in the work itself, and disappears the moment you switch tools.
Most AI tools can work with stored context, but only if you do the work of transferring it every time. Upload your style guide. Paste in a brief. Re-explain your audience. That overhead is real, and it compounds across every session, every campaign, every new piece of work. But situational context is harder to transfer. It’s fleeting, it changes constantly, and re-explaining it at the start of every session is exactly the kind of overhead that’s been quietly accumulating on your team’s plate.
What context-aware AI looks like in practice
The difference between context-blind and context-aware AI isn’t subtle. It changes what your team spends their time on.
Let’s go back to that launch week scenario. With a context-blind tool, you’re starting from scratch every session. You have to upload your style guide, paste in the brief, explain your audience, and provide the positioning work. The output comes back generic. You fix it. You prompt again. The AI keeps producing. The overhead keeps accumulating.
With context-aware AI, that session looks different. The tool already knows your brand voice, your campaign history, and the messaging framework your team is working within. You open a document and it can work off of the content you already have on the page. Your first draft is strong, on-brand, and needs little human intervention before it’s ready to ship.
The downstream effect is significant. Drafts need refinement, not rescue. Reviews focus on creative decisions rather than brand corrections. Work moves between your team without losing the context behind it. And the mental load of re-explaining, realigning, and re-reviewing starts to lift.
This is what AI maturity looks like in practice—and knowing where your team stands today is the first step to getting there.
Assess your team’s AI maturity
Most of the rework, the rewriting, and the review cycles that have quietly piled up on marketing teams trace back to the same root cause: The AI didn’t know enough about the work to get it right the first time. Closing that gap by building stored context into your workflows and giving your AI the situational awareness to work alongside your team is what reaching AI maturity actually requires.
The Marketing AI Maturity Assessment is designed to show you where to start. It maps where your team is across the three stages, where context is getting lost, and what the path forward looks like.
