7 Questions We Had Going Into Config Leadership Collective | Figma Blog
The post captures lessons from Figma’s Config Leadership Collective, where more than 1,300 design, product, and engineering leaders discussed leading through AI-driven change. Its central argument is that successful leadership depends less on rigid processes or tool expertise and more on adaptability, human-centered design, judgment, collaborative teams, and supported experimentation. ## Leading Change Through Experimentation - Leaders are learning alongside their teams as AI rapidly changes established workflows. - Rather than adopting fixed processes, they emphasize adaptability and continuous adjustment. - Executives are experimenting directly with new tools, prototyping ideas, and sharing failures. - Teams need permission to explore, take risks, and remain enthusiastic even when experiments fail. ## Preserving Human-Centered Design Fundamentals - AI has changed methods, but core principles remain important: - Understand users and their workflows. - Continue prioritizing craft and quality. - Design for people rather than simply following new tools. - Leaders warn against “chasing the tool” at the expense of human needs and thoughtful design. ## Expertise Is Moving Toward Judgment - AI can increasingly handle execution and task completion. - Human expertise is becoming more valuable in higher-order activities such as: - Taste - Discernment - Contextual decision-making - Evaluating and editing AI-generated work - Expertise now means selecting the best answer for a particular situation, not simply knowing a single correct answer. ## Restructuring Teams for the AI Era - AI is blurring traditional boundaries between design, product, engineering, and other disciplines. - Airbnb is organizing work into small, self-contained pods that resemble startups. - These pods combine core product roles with perspectives such as data science or business expertise. - Strong editing judgment, diverse viewpoints, and constructive disagreement are treated as essential. - Effective teams should be scrappy, vocal, ambitious, and willing to challenge one another. ## Helping Teams Adopt New Tools - Adoption requires education, infrastructure, and psychological safety—not just instructions to use AI. - Expedia is building dedicated support and training to help employees become fluent with AI tools. - OpenAI recommends starting with small, low-risk tasks instead of imposing large automation programs from the top down. - A simple use case, such as summarizing a long Slack thread, can demonstrate value and encourage broader adoption. The practical recommendation is to lead AI adoption as an ongoing learning process: experiment personally, preserve user-centered standards, hire for judgment and curiosity, build cross-functional teams, and introduce tools through manageable, well-supported steps.
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