What Actually Happens When You Bring AI Into a Creative Team
A year ago, I thought about AI mostly as a tool. Something that could help generate an image, write a headline, or speed up a task. Today, after spending months integrating AI into creative workflows, I see it very differently.
I use AI throughout my day, from reviewing creative work and understanding complicated briefs to challenging ideas, organizing client feedback, and helping teams work more efficiently. What started as experimenting with individual tools has gradually become a different way of thinking about the creative process.
And the more I use it, the more convinced I am of something that sounds a little contradictory: the more creative work AI can do, the more important human creativity and judgment become.
That doesn't mean every creative needs to become an AI expert. But I do think we need to rethink how we work, what we spend our time on, and how we share what we're learning with our teams.
Design: Start with your brain, not the prompt
Design is probably the most obvious place to see how much AI has changed. We can generate beautiful images in minutes, explore visual directions that would have taken days, and produce variations at a speed that was unimaginable just a few years ago.
But generating an image isn't the same as designing.
A good designer understands composition, hierarchy, storytelling, and brand. They know what should catch your attention first, what should disappear into the background, and how every element contributes to the message. AI can produce something technically impressive without understanding any of those decisions.
I've experienced this firsthand. You can spend time perfecting an image only to realize that the product is inaccurate, the brand details have changed, or the overall concept doesn't communicate what you intended. The image might be beautiful, but it still doesn't work.
That's why my approach is to design with your human brain first, then teach AI how you design. Not the other way around.
The interesting part is that working with AI is becoming a creative skill in itself. How you describe an idea, provide context, structure instructions, and evaluate the output makes an enormous difference. Two designers using the same tool can produce completely different results, not because one has access to better technology, but because one knows how to direct it.
And when someone figures out a better way to work, I think it's important to share that knowledge. Creative teams have always learned from one another through critiques, collaboration, and experimentation. AI shouldn't change that. If anything, it makes sharing our processes even more valuable.
The tool matters, of course. But knowing how to use it matters much more.
Copy: AI can write, but who's controlling the narrative?
My perspective on AI and copywriting has probably changed more than anything else.
A year ago, I understood why writers were skeptical. AI-generated copy often sounded generic, overly polished, and strangely repetitive. And while that still happens, the technology has improved enough that simply debating whether AI can write good copy feels like the wrong conversation.
What interests me now is how AI can participate in the thinking behind the writing.
Today, I can give it a creative brief, brand guidelines, previous campaigns, audience information, and client feedback. Instead of asking it to write something immediately, I can use it to understand the problem, explore possible directions, and challenge the work.
I might ask whether a message really answers the brief, whether a developer would care about a particular benefit, or how the same idea should change when we're speaking to an executive. Sometimes I'll ask it to explain why a headline feels weak or identify patterns in feedback that the team keeps receiving.
One of my favorite instructions is simply to tell AI to stop agreeing with me and challenge the idea instead.
Sometimes I write the first draft. Sometimes AI does. Most of the time, it's a combination of both. But I'm always controlling the narrative, reviewing the work, questioning the recommendations, and deciding what stays.
I don't see AI as something that works for me. I see it as something I work with. That distinction is important because the value isn't necessarily producing more copy. It's being able to explore more possibilities, compare different approaches, and get to stronger ideas without spending hours on every initial direction.
There's research supporting some of these productivity benefits. In a 2023 study published in Science, researchers found that professionals using ChatGPT completed certain writing tasks approximately 40% faster, while independent evaluators rated their work 18% higher in quality.
Of course, writing a business document isn't the same as developing a campaign or building a brand. But the findings help explain what I've been experiencing: when AI reduces the time spent on certain parts of the writing process, we have more room to focus on the thinking behind it.
And for me, that's where the real value is.
Operations: What if creative teams could actually remember what they've learned?
This might be the area where I see the greatest long-term potential.
Creative teams generate an enormous amount of knowledge, but we don't always do a very good job of keeping it. A client gives feedback on a campaign, the team makes adjustments, and everyone learns something about what works for that brand. A few weeks later, a different project begins, and we find ourselves having almost the same conversation again.
The information exists somewhere, usually buried in emails, presentations, comments, meeting notes, or someone's memory. But finding it and applying it consistently is another story.
I've started thinking less about individual AI prompts and more about building systems that can capture what we're learning and make it useful for future work.
For example, I've been experimenting with analyzing client feedback across scripts to understand recurring preferences, common mistakes, and the changes that ultimately lead to approval. Those observations can then become instructions for specialized AI agents that help writers and reviewers approach the next project with more context.
It's a relatively simple idea, but it changes how we think about creative operations. Instead of every project starting from scratch, we're building on what the team has already learned.
I like to think of this as creative memory.
And there's a broader business case for it. In its research on the economic potential of generative AI, McKinsey identifies knowledge management as an important opportunity for the technology, particularly when it comes to helping employees find, organize, and use information spread across an organization.
For a creative team working across multiple brands, markets, languages, and campaigns, the potential is significant. Imagine having a system that helps a new team member understand how a client thinks, what they've approved in the past, which messages resonate with their audience, and what mistakes to avoid.
It doesn't eliminate the need for experience or relationships. It makes the knowledge we've already developed easier to access and apply.
Of course, this only works if the information is reliable, current, and appropriately managed. Client feedback can be subjective, brand preferences change, and an AI system shouldn't turn one person's comment into a permanent creative rule. Someone still needs to review what the system learns and decide whether it's useful.
But when it's done well, AI becomes much more than a shortcut. It becomes part of how a team gets better over time.
AI is also changing how quickly we can learn
There's another benefit I've noticed that doesn't fit neatly into design, copy, or operations, but has become an important part of how I work.
As a Creative Director, I'm constantly moving between different industries, products, audiences, and business challenges. Some subjects are familiar, while others can be incredibly technical. And although I don't need to become an expert in every industry, I do need to understand enough to ask the right questions and make informed creative decisions.
In the past, getting comfortable with a complicated subject could involve hours of research, reading documentation, or waiting for someone to explain the fundamentals. Now, AI can help me get oriented much faster.
When I receive a technical brief, I might ask AI to explain the subject in simple terms, identify what matters most to the audience, or help me prepare questions for a subject matter expert. I can explore the same information from different perspectives until I understand the problem well enough to contribute meaningfully.
Five minutes later, I haven't become an expert, but I'm informed enough to have a better conversation.
This idea of making knowledge more accessible is supported by research. A study of more than 5,000 customer support agents found that access to an AI assistant increased productivity by an average of 14%, with particularly strong benefits for less-experienced workers. The researchers observed that AI appeared to help newer employees benefit from knowledge and practices associated with more experienced colleagues.
It's a different industry, but the underlying principle is interesting for creative teams. AI can help us access knowledge we haven't personally accumulated yet, which can make it easier to contribute, collaborate, and learn.
That doesn't mean we should stop asking questions or relying on experts. Quite the opposite. I think AI can help us ask better questions because we arrive at those conversations with a stronger foundation.
The creative job isn't disappearing. It's changing.
When I look at how AI is affecting design, copywriting, and creative operations, I keep coming back to the same conclusion.
As AI becomes capable of doing more of the execution, the importance of direction increases. Someone still needs to define the problem, understand the audience, recognize a good idea, question the output, and make the final decision.
And sometimes, that decision is not to use AI at all.
One study that I find particularly interesting is the Harvard Business School and Boston Consulting Group experiment on AI-assisted knowledge work. Researchers found that consultants using GPT-4 performed significantly better on tasks within the technology's capabilities. However, when participants worked on a task outside those capabilities, those using AI were more likely to arrive at incorrect solutions.
It's a useful reminder that being able to generate an answer doesn't mean the answer is right.
The same applies to creative work. AI can give us a beautiful design that doesn't follow the brand guidelines, persuasive copy that misses the audience, or a confident recommendation based on incomplete information.
Without the experience to recognize those problems, working faster doesn't necessarily mean working better.
This is why I believe creative judgment will become more valuable as AI improves. The people who get the most out of these tools won't necessarily be the ones generating the highest volume of content. They'll be the ones who understand what should be created, why it matters, and how to recognize when something isn't working.
I also think creative leadership is evolving. We're no longer just directing designers, writers, and production teams. We're increasingly helping shape the systems, workflows, and shared knowledge that allow those teams to do better work.
That requires curiosity, experimentation, and a willingness to change how we've always done things. It also requires the experience to know when technology is genuinely improving the work and when it's simply adding another layer of complexity.
After spending months integrating AI into creative teams, I don't think the biggest opportunity is producing more content with fewer people. I think it's building teams that are more informed, more independent, and better equipped to solve creative problems.
AI isn't replacing the need for creative thinking. It's changing where we apply it.
And that's the part I'm finding most interesting.
