Contrary to expectations, job candidates found it easier to talk to an AI interviewer. The lower pressure of a non-human interaction allowed them to relax, be more open, and talk more freely about their experiences, leading to better outcomes.
In a highly collaborative and fast-paced environment, assign explicit ownership for every feature, no matter how small. The goal isn't to assign blame for failures but to empower individuals with the agency to make decisions, build consensus, and see their work through to completion.
Adopted from visual identity design, this framework involves building products while anticipating future, unknown contexts. It means considering how a user's mood, location, or time of day might affect their experience and designing flexible systems to meet them where they are.
Instead of siloing roles, encourage engineers to design and designers to code. This cross-functional approach breaks down artificial barriers and helps the entire team think more holistically about the end-to-end user experience, as a real user does not see these internal divisions.
A fascinating meta-learning loop emerged where an LLM provides real-time 'quality checks' to human subject-matter experts. This helps them learn the novel skill of how to effectively teach and 'stump' another AI, bridging the gap between their domain expertise and the mechanics of model training.
AI and cataloging tools have compressed the competitive research phase from days to minutes. This frees designers from tactical UI comparison and empowers them to focus on higher-level strategic work: crafting product narrative and system architecture, a role previously reserved for senior leadership.
A key competitive advantage wasn't just the user network, but the sophisticated internal tools built for the operations team. Investing early in a flexible, 'drag-and-drop' system for creating complex AI training tasks allowed them to pivot quickly and meet diverse client needs, a capability competitors lacked.