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4 team rituals that drive AI adoption Elevating AI’s impact from individual productivity gains to team-wide wins requires meeting people where they are. Here’s how teams at Figma, Sephora, Mercari, and Atlassian are making it easier for people to drive real value with AI. Share 4 team rituals that drive AI adoption Hero illustration by James Marshall Today, a lot of AI experimentation is siloed in individual workflows: A few skilled power users improve their own workflows, while less fluent product builders More than 90% of product builders say AI skills are essential to their future success. But what if we’re focused on the wrong ones? AI fluency isn't the finish line To foster team-wide adoption, teams need to create forums centered on peer support where both beginners and skilled practitioners can learn, practice, and refine AI skills in a low-pressure environment. Here are four examples, from small study groups to prototype-first critiques, that teams have had success with. Learn more about driving AI adoption across an organization. Figma runs peer-led study groups Jake Geller, a design operations manager at Figma, launched a study-group pilot he called “AI Collective” to help designers develop their AI skills. “There’s a wide variance of technical fluency and AI fluency on the design team,” he says. “We wanted to raise the baseline and support everyone.” To understand what that support should look like, Jake asked designers which skills they hoped to develop and what kind of environment would help them learn best. Their interests varied—some wanted to go deep and become fluent in one tool like Figma Make The handoff from design to code doesn't have to be a one-way street. This workflow shows how a designer can connect a codebase to Figma Make, make the changes directly, and bring the whole team into review—right up to the PR. Community publishing is now live for Weave tools. Here are a few tools that stand out, from turning a moodboard into a realistic room mockup to previewing your next nail set. Workflow lab: Deploying designs directly with Figma Make Try these 5 Weave tools—and share your own Dive deeper into Figma’s ritual: Goal: Bring less AI-fluent designers up to speed through hands-on practice and support from their peers. Format: Jake set up five peer-led study groups organized by the topics designers had chosen. Some designers volunteered to lead, while other participants chose which group to join. Each group met for one hour a week over six weeks and used Slack to keep the conversation going between sessions. Leads used a flexible planning template that suggested 20 minutes of teaching each skill and 40 minutes of practice. Takeaway: A tangible goal, like learning how to build a Figma plugin, can make learning more engaging and impactful by giving participants something specific to work toward. Peer-led support makes it easier to ask questions and get unstuck. How to try this with your team: Find out which AI tools AI tools are changing how teams build products—from where they start to what carries through to production. Here's what that shift looks like across four organizations. 4 new ways to go from idea to product with AI tools Sephora shares what didn’t work Sneha Narahalli, senior vice president of technology at Sephora, hosts a biweekly forum where builders across the organization share what they’re building with AI, whether for work or as a personal project. In the first few sessions, presenters only shared polished work. “We were sharing the final glamorous AI-generated outcome,” Sneha says. But teams shared that they felt overwhelmed when they saw well-executed AI projects from their peers and still weren’t sure how to get there themselves. In response, the organization decided to change the format of the biweekly forum—now, presenters walk through the process behind the work, including what they tried and what didn’t work. “Learnings aren’t just the good stuff,” Sneha says. “Learning means you tried this and it didn’t work, and now you know what not to do next time.” Dive deeper into Sephora’s ritual: Goal: Make AI experimentation feel more approachable and share the process behind AI projects so the whole team can learn from it. Format: Every two weeks, people share something they’re building with AI for work or as a personal project. A product builder might share how they use a vibe coding tool to create an app that interprets dreams, while others might offer tips for creating skills From sharing your best thinking to learning from your team’s go-to prompts, here’s what skills unlock in the Figma agent. Got skills? Make the Figma agent a better collaborator Takeaway: Sharing the process, step by step, helps those less familiar with AI tools find a jumping-off point. The forum also keeps learning in the hands of peers, which Sneha finds gives others the confidence to try it themselves. “Hearing from a peer and seeing how it makes their life easier is way more powerful than top-down messaging,” she says. Learn more about Sneha Narahalli’s strategies for driving AI adoption and leading teams through ambiguity. How to try this with your team: Give product builders a place to share AI experiments. Set aside 10 minutes at the beginning of an existing meeting, create a dedicated session, or encourage people to share what they’re learning in a new Slack channel. At Figma, product builders share their learnings from AI during a segment in bi-weekly company all-hands meetings and a Slack channel, both titled “How I AI.” Mercari cross-trains across functions At Mercari, Principal Designer Aki Alkhatib’s team is experimenting with ways to close the knowledge gap between designers and developers. “AI makes it easier to build, but people don’t automatically know how to judge what they’ve made,” Aki says. “They’re expanding into areas where they don’t have as much knowledge.” Dive deeper into Mercari’s ritual: Goal: Help product builders judge and improve Having the right tools can only take you so far. To become a master of your craft, you need to hone a point of view. You never stop cultivating taste Format: Mercari runs hands-on sessions where designers learn engineering fundamentals, including how to write code or use an AI coding tool and set up a GitHub repository. In turn, developers learn foundational design principles like visual hierarchy. With this training, product builders develop enough fluency to produce a useful starting point outside their own discipline, allowing them to more efficiently collaborate with cross-functional teams. Takeaway: Cross-training gives people enough context to take an idea further outside their discipline. For example, a developer could use Figma Make to draft a basic interface, then apply what they’ve learned about visual hierarchy to make the most important information stand out before a designer reviews it. Domain experts can then use their deeper knowledge to review and refine the work. “The starting point could come from anyone,” says Aki. “Then we have the domain experts looking at it and making sure that it’s up to standard.” How to try this with your team: Choose one basic design task for developers to learn and one basic engineering task for designers. Run a few hands-on sessions where experts teach the fundamentals, participants try each task, and everyone reviews the results together. Atlassian opens every crit with a prototype At Atlassian, Chief Design Officer Charlie Sutton knows that team-wide AI use requires more than education alone. When teams started experimenting with AI prototyping tools in earnest, he noticed that designers often explained the context and rationale before showing a prototype. This meant the team saw prototypes later in the process, if at all. So, he decided to adopt a new rule: Show the prototype first and explain it afterward. Dive deeper into Atlassian’s ritual: Goal: Push product builders to get familiar with prototypes and see their value through regular practice. Format: A designer starts the review by letting the group click through a prototype, which can be created in a tool like Figma Make. Only afterward do they explain the goals, research, and rationale behind it. This helps teams see the value of prototyping: Instead of discussing a product in the abstract, they can let users click through the experience and see how it would behave in practice. As Charlie puts it: “Show me the thing, help me play with it as a prototype, and then explain to me why you made that decision.” Because designers have to show up to every crit with a prototype, it gives them practice using an AI tool regularly. Takeaway: “Sometimes, simple rules are what make complex changes possible,” Charlie says. His team still needs reminders, but applying the rule at every crit keeps reinforcing the habit. How to try this with your team: Test a prototype-first rule in one recurring critique for a month. Give the group time to click through the prototype before the presenter shares any context. Continue to reinforce this practice until it becomes routine. Driving AI adoption across an organization takes more than giving people access to the tools. By experimenting with new rituals and ways of learning together, more product builders can learn to use AI as a team, not just as individuals, and make it part of how their work gets done. Emma Webster is a writer and editor on Figma’s Story Studio team. Previously, she’s worked as a writer at Faire and Audley Travel.
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