figmaJul 9, 2026

How to Lead Design Teams Through the AI Era | Figma Blog (opens in new tab)

Jen Dunnam argues that design leaders should respond to AI-driven change with calm, deliberate experimentation rather than panic. The fundamentals of design remain human-centered, so teams should prioritize clear thinking, strong principles, and the ability to turn insights into products. Her approach emphasizes investing in emerging talent while hiring and developing designers who can challenge assumptions. ## Lead with Calm - Leaders should steady their teams instead of adding to the urgency already felt by ambitious designers. - Break AI-related change into manageable steps: - Choose an approach. - Experiment with appropriate tools. - Refine design principles. - Learn from the results. - Designers should avoid chasing every new capability simply because it is novel or impressive. - AI may transform workflows, but designing for human needs remains the central responsibility. ## Hire for Critical Thinking - Dunnam would invest more heavily in designers fresh out of school, many of whom are disadvantaged by today’s pressure to ship quickly. - Pair early-career designers with experienced practitioners who can help turn ideas into shippable products. - Look for researchers who can move beyond gathering insights and contribute decisively to product direction. - Critical thinking has become especially valuable as AI tools make polished but potentially shallow solutions easier to produce. - Interviewers should ask candidates: - Where did they disagree with a stakeholder? - How did they push back? - What product decision still bothers them? - These questions reveal whether candidates can challenge attractive but poorly reasoned solutions. Dunnam’s practical recommendation is to keep teams grounded in human-centered design, combine emerging and experienced talent, and hire people with the judgment to question what appears easy, polished, or technologically exciting.

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figmaMar 10, 2026

Vishal Kapoor’s 10 Rules for Building Honest Products with AI | Figma Blog (opens in new tab)

AI product development is ultimately a trust challenge, not merely a technical one. Vishal Kapoor argues that AI should accelerate exploration and execution without replacing human judgment, empathy, or accountability. His approach centers on building products that remain transparent, secure, emotionally aware, and honest—especially in sensitive areas such as personal finance. ## Start with First-Principles Thinking - Break complex problems into their fundamental components before reaching for an AI solution. - AI can accelerate ideation and iteration, but it cannot replace human intuition, taste, or a distinctive product perspective. - Question basic assumptions to uncover better alternatives. For example, Affirm challenges why customers receive three payment-plan options rather than one, five, or a customizable plan. - Thoughtful disagreement among people remains essential for generating meaningful insights; AI is best used to explore possibilities more quickly. ## Stay Close to Human Emotions - Product teams should regularly observe customers, conduct UX research, read app-store reviews, monitor social media, and speak directly with users. - Metrics and dashboards identify patterns, but they do not fully explain the emotions behind customer behavior. - Financial products especially require sensitivity to anxiety, frustration, trust, and relief—not just transactional outcomes. - Affirm uses an internal AI tool called Pluto to investigate recent customer disappointments, while still relying on human observation and empathy to interpret those experiences. ## Treat AI as a Teammate - AI is neither a guaranteed productivity multiplier nor an inevitable replacement for employees; it is another participant in a collaborative product-development process. - Tools such as Figma Make help teams convert customer insights into prototypes and test ideas faster. - AI can audit large numbers of screens and interaction patterns across web, mobile, and desktop experiences, identifying outdated or inconsistent designs. - Moving repetitive auditing and prototyping work from engineers to designers and product managers increases iteration speed and creates more room for creativity. ## Test the Edge Cases - Trustworthy products cannot be designed only around the happy path. - Teams should deliberately explore unusual inputs, failure modes, and unexpected customer situations rather than assuming normal usage. - The article begins this rule by emphasizing that authentic product quality depends on examining the difficult and overlooked scenarios where users are most likely to encounter confusion or harm. The overall recommendation is to use AI aggressively for exploration, prototyping, and repetitive analysis—but keep humans responsible for defining the problem, understanding customers, challenging assumptions, and ensuring the final product is honest.

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figmaOct 14, 2020

Learning and failing as a team | Figma Blog (opens in new tab)

Config Europe highlighted how better products emerge from better teamwork. Figma argues that inclusive processes, cross-functional collaboration, and a willingness to fail openly help teams build more human-centered experiences. The success of its Variants feature demonstrates that iterative testing and diverse perspectives can turn early shortcomings into meaningful improvements. ## Learning Through Collaboration - Figma’s virtual Config Europe conference explored both product craft and team culture. - Sessions covered: - Accessibility-first design - Keeping design aligned with code - Technology ethics - Collaboration and shared failure - The central theme was balancing product function with human feeling while working more effectively together. ## Expanding the Team - UX designer Declan Talbert presents design systems as services for entire product teams, not merely pattern libraries for designers. - An inclusive design system can contain: - UI components - Accessibility guidance - Data resources - Project-management tools - Designers, developers, product managers, and other contributors should all be able to participate. - Broader collaboration and diverse skills lead to more human-centered products and services. ## Failing Together on Variants - Product Manager Kelsey Whelan and Product Designer Nikolas Klein describe shared failure as a major factor in the development of Figma’s Variants feature. - Early testing showed that the feature was powerful but difficult to approach, contradicting the team’s initial assumptions. - Usability testing expanded from a planned couple of weeks into four rounds over six weeks. - Figma invited employees from different roles—including design advocates, product educators, and engineering managers—to participate remotely through Zoom. - Participants identified usability problems and bugs, while a dedicated Slack channel helped coordinate fixes. - Repeated testing made the feature more intuitive and reinforced the idea of “failing forward”: using visible mistakes to improve the product and strengthen team culture. Figma’s practical recommendation is to open product development to more people, test ideas early, and treat failure as shared information rather than individual blame.

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