Beyond tool mastery: the collaborative skills AI makes essential
Ask product builders what it takes to succeed, and AI fluency tops the list. In Figma's 2026 AI report, more than 90% of surveyed designers, developers, and product managers said learning to work with AI is essential to their future success. Tool proficiency clearly matters—it helps people get hired, move faster, and stay confident during rapid change.
But tooling is only one layer. The work that happens around the tools—building shared systems, pushing ideas with the right collaborators, and steering conversations toward decisions—carries more weight as AI accelerates individual output. These are the skills that translate one person's speed into a team's momentum.
Build for your team, not just yourself
AI lets individuals generate and iterate faster, but that speed compounds when it's embedded in how the whole team works. Custom internal tools—a prototyping agent, a brand plugin, or a shared prompt library—turn one person's expertise into something everyone can use and trust.
Figma's own teams offer examples. During development of the AI report, researcher Shane Johnston coded an interactive website to present survey data, giving cross-functional stakeholders a hands-on way to explore it. "The context and data weren't just locked in my head or on my machine," he says. The Brand Studio took a similar approach for Config, building an image-effect generator in Figma Make so anyone could apply on-brand textures to photos or designs with a click. Elsewhere, a surveyed design manager built a shared prototyping playground where anyone—regardless of role—could create code-based prototypes for testing with customers.
Spotting friction and removing it has always been core product instinct. AI expands who can act on that instinct. Rather than one person moving ten times faster alone, the goal is moving a whole team ten times faster together.
Steer options toward decisions
Generating dozens of directions is now trivial. The harder part is turning those directions into a decision. That elevates facilitation: knowing whom to bring in, what context they need, and how to push a conversation to a close.
Good facilitation starts before the meeting. It means inviting voices beyond the core project team—people with contrarian opinions, historical knowledge, or risk expertise. One leader learned this the hard way after a team's internal app exposed sensitive company information. The problem surfaced late, and a data governance expert brought in earlier could have caught it.
Options should arrive with context. Figma PMs record a Loom video walking through each prototype flow or share an annotated FigJam file laying out trade-offs. That way, live review meetings focus on judgment rather than explanation. During the meeting, the facilitator's job is to land the plane: invite quiet voices in, probe unclear recommendations, ask forward-moving questions, and confirm next steps before everyone disperses.
Normalize sharing unfinished work
AI adoption is uneven across most organizations. Some team members have been experimenting since the first LLMs; others are still orienting themselves.
Left unattended, the gap widens—the fast get faster while others struggle to keep up. Bridging it requires shared curiosity and willingness to learn from each other's experiments, including failures. Adam Morris, VP of Design at The Economist, describes the right mindset as "sustained curiosity—which can be both disorienting and energizing at the same time."
The practical version is simple: make sharing routine. Run crits on half-baked prototypes, talk about side projects, or hold hackathons where people trade prompts and insights. At Figma, teams exchange AI challenges and tips in a dedicated Slack channel and at all-hands meetings. One person's discovery becomes everyone's advantage when sharing is baked into the culture.
None of this diminishes the importance of AI tool proficiency. It just reframes it as table stakes. As tools become commoditized, the differentiator will be the human skills wrapped around them: building shared infrastructure, making decisions from abundant options, and learning openly together.
