How Figma integrates AI to transform design and empower creatives

Our new Executive Function series features perspectives from leaders driving transformation through AI.

Figma is where teams come together to turn ideas into the world’s best digital products and experiences. We spoke with Figma’s David Kossnick, Head of AI Products, about the impact of AI in design, empowering creativity, and building AI fluency for Figma employees. 

You’ve described AI as both a platform shift and a core capability. How is AI changing design, and how is Figma positioning itself in that transformation?

As AI makes it easier than ever to create digital products, great design will increasingly be a key differentiator. But design isn’t just pixels; it’s craft: empathy, workflow understanding, and problem-solving.

That’s why AI is embedded throughout Figma—from in-product text editing and image generation to auto-renaming layers and site visuals—helping make creation faster, more intuitive, and accessible to more people.

At the same time, AI is also a platform shift. Figma is purpose-built for building digital products. This lets us rethink workflows from first principles.

One example is Figma Make⁠(opens in a new window)—a prompt-to-app tool that generates production-grade code from language, images, or structured frames. It gives coders and non-coders alike—designers, PMs, engineers, marketers—a way to go further, to prototype and express ideas without being blocked by technical barriers.

There’s a lot of talk about AI as a co-pilot, not a replacement. How do you see that dynamic empowering creativity?

Figma stands out for its deep commitment to craft—giving users full control to refine every detail. With AI, we’ve expanded beyond the visual layer to include language, visual, and code—adding tools like a code composer and “code layers” that let users write and publish AI-assisted code natively.

AI agents can handle the busywork, but many tools limit customization after that. At Figma, you can fully edit every layer—language, visual, and code—to match your vision and uphold craft. We also support cross-modality workflows, so whether you’re strongest in code, design, or language, you can work your way—like going full-stack without losing your specialty.

At the end of the day, the products made in Figma are for humans. And humans bring judgment, empathy, and taste—qualities that make them the true pilot, not just the co-pilot.

Figma Make and the Dev Mode MCP Server were major steps in integrating AI into end-to-end workflows. What have you learned about how designers and developers want to interact with AI through code?

Designer-developer collaboration hinges on shipping what actually works—not just avoiding miscommunication, but ensuring user value. Figma Make helps teams validate and test many possible ideas so when they do align on a solution, there’s strong conviction to build the right thing.

Dev Mode⁠(opens in a new window) streamlined handoff with structured data like CSS and tokens, and MCP⁠(opens in a new window) takes it further by letting developers invoke a coding agent that translates mocks into production-ready code with full context—no manual copy-pasting required.

Even though Make is built primarily for prototyping, designers can often prompt interactions so precisely that engineers copy the code directly—making it start to become a handoff artifact for engineering.

More broadly, Figma has always been multiplayer by design, unlike early AI tools which were largely single-player. Now, we’re moving toward more collaborative AI experiences that invite others into the creative process.

How are you thinking about AI tools that support collaboration and the idea of multiplayer?

Multiplayer is core to Figma, and tools like Figma Make and code layers are built to support real-time collaboration—even with AI. Two people can work in the same file, see each other’s avatars, and co-create with an AI assistant, turning meetings into shared, interactive building sessions.

Image generation has also become a highlight in FigJam and Slides, enabling teams to co-create brand-aligned visuals or iterate side-by-side. There’s a cultural dimension too—like our tradition of making FigJam anniversary cards, where teammates remix avatars using OpenAI’s image editing to create playful, personalized tributes. These creative rituals foster connection and team spirit in ways most tools can’t.

As more design processes—like layer naming, copywriting, visual search, and generation—become AI-powered, how do you see the role of the professional designer evolving?

Craft remains the most essential skill—empathy, taste, and the ability to explore and refine. As the cost of trying ideas drops, people can go deeper on what works, making every detail—from animations to interactions—an opportunity for excellence. While noise will increase, great craft will stand out.

We’re also seeing a shift from implementers to problem-solvers, with roles merging and more people becoming makers. Designers are writing code, and the future belongs to vision carriers—those who can take an idea from concept to execution on their own.

I have a colleague in media and entertainment who describes the high upfront cost of idea generation and pitching—it required so much effort that many great ideas were filtered out before they even had a chance. Now, with AI, that bottleneck is easing. We’re seeing a proliferation of ideas because creatives can explore and share much more freely.

It reminds me of Doctor Strange in the Marvel universe—how he sees all possible futures. That’s what AI is becoming for design: a way to explore countless paths and pick the best one for a given problem.

What kinds of users or use cases do you think Figma’s AI will unlock that weren’t possible before?

We’re already seeing amazing examples—even before launch. During internal testing, someone on the HR team with no coding or design background discovered a Workday API and used Figma Make for just two hours to build a game: it showed four faces and names pulled from Workday, and you had to match them—a fun way to help new employees get to know teammates. It’s now part of our onboarding process.

This was an idea no internal tools team would have ever prioritized, but it came to life because AI lowered the barrier. It showed that non-technical people with great ideas can now build real, usable tools—sometimes even deployable—without needing an engineering team.

We’re seeing a lot of unexpected use cases, and it’s incredibly inspiring. Tools like Figma Make and Figma Design let people express and activate ideas that would’ve otherwise stayed dormant.

How are you building AI fluency—those “aha” moments when people realize they can do something they couldn’t before? Any learnings so far?

Dogfooding is central to our culture, and we went all-in with Figma Make. We ran the “Great Figma Bake Off”—a company-wide competition to build cool projects, with live jam sessions in every time zone. That hands-on support helped AI-curious employees build confidence, especially those new to these tools. Social incentives and live guidance made a big difference in helping people engage.

Beyond that, we rolled out ChatGPT Enterprise across the company. It’s been transformative—go-to-market teams use it for refining pitches, drafting emails, and more, all in a secure, privacy-conscious environment.

We also host Maker Weeks—weeklong hackathons open to everyone, not just product teams. People build everything from videos and help docs to Slack-integrated GPTs. It gives everyone permission to try, fail, and learn—lowering the barrier to hands-on experimentation, especially for those outside core technical roles.

Is this more philosophical—creating a culture of AI fluency—or are there ways you’re measuring progress?

AI fluency at Figma starts with culture. We hire people who are eager to experiment and explore new tools, and we support that with dedicated time and budget for learning—no mandates required.

We highlight success stories, like an HR team building a Workday-powered game, to show that even a 10-minute experiment can spark real impact. That first step is often the hardest.

To support safe exploration, we’ve created a compliance fast path for experimental tools—with guardrails on data use—so teams can test new AI without friction. Most tools won’t work perfectly, but lowering the cost of trying helps uncover real value and fuels innovation across the org.

You’ve shared great insights on building internal AI fluency. But what about the consumer side—how should companies approach integrating AI into their products and experiences?

As both AI users and builders, we’ve learned that grassroots experimentation drives adoption. Employees began using tools like ChatGPT informally, which led to demand for a secure, supported path—ultimately prompting our rollout of ChatGPT Enterprise.

The big takeaway: once people try AI workflows and realize how easy they are, they feel empowered to build. That shift in mindset is key to scaling meaningful AI adoption—both inside the company and for customers.

To wrap up—how are you personally using AI in your workflows at Figma?

I use ChatGPT daily for everything from cleaning up review notes and drafting comms to deep research—often prompting it with “how is this problem typically solved?” to quickly explore solution spaces. 

I also rely on Figma Make for prototyping and idea exploration, and Slack AI to summarize complex threads and stay aligned across the org. Lastly, I use Grammarly constantly—it may not feel like AI, but it quietly improves my writing throughout the day with just a click.

Figma uses OpenAI APIs to power FigJam AI, as well as its image generation capabilities on its platform. It has also deployed ChatGPT Enterprise across its organization to enable AI fluency for its workforce.