Why AI Makes Design Skills More Valuable Than Ever • Creativity in the Age of AI, Part Two

Like everywhere, our d.school instructors have been exploring how generative AI might reshape creativity, design, and human expression. This fall one of our longtime collaborators, Glenn Fajardo, launched a series of hands-on experiments with faculty, staff, and students around how generative AI can be a catalyst for creativity. These explorations have been collaborative, reflective, and are still unfolding. Here's what he found . . . 

Why AI Makes Design Skills More Valuable Than Ever

Creativity in the Age of AI is not about making the process easy. And it’s not just about getting good at using AI tools. 

Yes, it’s helpful to learn how to prompt effectively. Yes, it’s worth experimenting with tools like Midjourney, Runway, or NotebookLM. But if we stop there, we risk mistaking tool proficiency for creative facility. In June 2025, it feels like “AI literacy” efforts focus on how to write a good prompt, or how to use tools X, Y, and Z. I believe we need to focus on something more foundational.

If we’re aiming to go farther and dive deeper, where should we be focusing more of our time and energy? What kinds of human effort should we value? What kinds of human effort actually make a difference? 

New technologies, such as AI, change what humans need to be good at.

Consider writing. It’s easy to forget that writing is a human-made technology that extends our capabilities.

In Plato’s dialogue “Phaedrus,” Socrates fretted that the invention of writing will have negative effects on people’s thinking, that writing “will produce forgetfulness in the minds of those who learn to use it, because they will not practice their memory.”

Today, we’d all agree that writing has helped human thinking more than it has hurt it, but writing did indeed change how people think. 

As Walter Ong describes in his book, Orality and Literacy: The Technologizing of the Word, writing restructures human consciousness in fundamental ways, leading to different modes of thinking, memory, and expression compared to those in primarily oral cultures. Writing externalizes thought in new ways that enable reflection, analytical abstraction, critical analysis, and historical perspective. Over time, people put less effort into developing memory, oration, and thinking on the spot and more effort into cultivating analytical thinking, abstract reasoning, and reflective habits.

To put it in writing: Writing changed what humans needed to be good at. 

If we think of creativity in the age of AI as simply becoming proficient with the tools, we’re missing the bigger picture. That’s like thinking writing is just about mastering handwriting, spelling, and grammar.

So what skills might humans need to sharpen in the age of AI?

Curiosity, experimentation, noticing, and reflection—the mindsets and skills of design—are essential for creativity in the age of AI. 

Now I know it’s awfully convenient for a design educator to say this! But these design mindsets can actually help us thrive in the age of AI and may even become the real differentiators of creative work going forward.

Following threads of curiosity. Reflecting, iterating, exploring. These are skills that help us to go farther and deeper in the creative journey. And when AI is paired with these mindsets it can help catalyze our human creative exploration.

Let’s use curiosity as an example. Say you’re trying to find a meaningful gift for a friend, and you try generating ideas with AI. Even after you include a decent amount of information in your first prompt, you get some generic suggestions. But instead of getting frustrated, you get curious about why they feel off. That curiosity becomes fuel. You follow your reactions, play with new directions, and shape your thinking as you go. Sometimes what starts out feeling flat can lead somewhere surprisingly personal . . . if you get curious.

What separates a good experimenter from a great one?

In creative work, AI can also help supercharge what designers call a “build to think” approach—taking abstract ideas and quickly giving them some more tangible form (physical or virtual), so that you can interact with them in a different way. 

In the early days of the d.school, a core justification for the “build to think” approach was that it was cheaper to build a quick prototype than to go through an elaborate planning process. Making became less about a perfect plan and more about sparking learning: surfacing insights, testing assumptions, and inviting feedback.

Now, with AI in the mix, the threshold for cheap and fast experimentation drops even further. The truly skillful experimenters will be those who consistently produce the more novel, innovative, and creative results. This raises an interesting question: If almost anyone can experiment quickly and at scale, what distinguishes a great experimenter? 

Perhaps it’s the ability to pose better questions. The ability to notice unexpected patterns and turn them into insight. The ability to create sharper contrasts between options. Perhaps skillful experimenters will learn how to navigate a wider range of possibilities.

Since AI makes it so easy to generate many possibilities, we often skim ideas superficially, similar to how we mindlessly scroll through social media feeds. We need to cultivate more curiosity and care so those possibilities don’t drift by before we’ve had a chance to cultivate them into something meaningful. 

Another challenge is what psychologist Barry Schwartz calls “choice overload.” While more choices might seem beneficial, they can lead to anxiety and decision paralysis. 

One way to navigate overwhelming abundance is to intentionally create useful variation. 

With AI’s ability to create an abundance of possibilities, the quality of the experiment itself becomes more important so that we are creating not just more options, but also more useful differences between them.

To get there, we may need to use AI in ways that deliberately introduce contrast, tension, or surprise. That might mean setting unexpected constraints. Or bringing in unusual influences. Or drawing inspiration from distant domains.

This is where the idea of far transfer can become especially productive. When we guide AI to explore patterns or principles from fields that seem unrelated at first—say, using the physics of soap bubbles to inspire app design or applying stand-up comedy metaphors to climate policy—we often uncover connections we wouldn’t have noticed otherwise. Skillfully used, cross-pollination can make things weird in all the right ways.

Creating variation isn’t just about stretching outward. It’s also about tuning inward. 

It’s about noticing your own reactions, catching those small sparks of surprise or delight. Divergent thinking isn’t about choosing the “best” idea from a long list. It’s an iterative process of combining, remixing, and reframing ideas as you go. 

These skills help cultivate the kind of variety that nudges you into new perspectives. The kind that gets you to say, “Huh… I hadn’t thought about it like that before.” And these are becoming even more essential for creativity in the age of AI. They are core to navigating a world where AI can generate endless options but cannot always help us discern what matters.

What’s next for creativity in the age of AI?

You can explore what this feels like in practice through an activity we developed called Chat about Chat. 

It’s not just the mindsets and skills of design that I think humans will need to get good at, get better at in the age of AI. (It’d be awfully, awfully convenient for a design educator to say the mindsets and skills of design were the only thing 😄). In Part Three, we’ll explore abilities beyond traditional design skills that might also be key to thriving creatively in the Age of AI.

CREDITS

Many thanks to Jenn Brown for her persistently collaborative editing; to Scott Doorley and Grace Hawthorne for wading through the early, mushy drafts and pointing out paths to traction; to Seamus Harte for supporting related community experiments; and to John Mitchell for his continuing collaboration on this topic.