Boulton & Watt built an internal AI agent that processes customer interview transcripts. It maps findings to core hypotheses, highlighting supporting and contradicting evidence. This keeps the team rigorous and fact-based, counteracting natural founder bias during the discovery process.

Ramp built an AI agent that sifts through Gong recordings, Salesforce notes, support tickets, and chats to answer any product question. This automates the work of an entire team, turning days of research into an eight-minute query to identify key customer pain points and roadmap priorities.

Product managers can use coding agents like Codex for self-service technical discovery. Instead of interrupting engineers with questions, they can ask the AI about the codebase, feature status, or implementation details, increasing their autonomy and team efficiency.

Design prototypes not just for user validation, but as internal "laboratories." By exposing system prompts and underlying data in the UI, you can demystify the AI, foster cross-functional collaboration, and accelerate internal alignment and learning.

Expensive user research often sits unused in documents. By ingesting this static data, you can create interactive AI chatbot personas. This allows product and marketing teams to "talk to" their customers in real-time to test ad copy, features, and messaging, making research continuously actionable.

Stripe built "Protodash," an internal tool that allows designers, PMs, and engineers to quickly create high-fidelity AI prototypes that mirror the real product. This removes the bottleneck of needing engineering for early exploration and empowers proactive, cross-functional ideation.

Contrary to assumption, the design process at OpenAI isn't about planning for a distant future. It's a fast-paced environment where designers work in close concert with the latest research advancements, adapting to new capabilities as they emerge.

At OpenAI, the development cycle is accelerated by a practice called "vibe coding." Designers and PMs build functional prototypes directly with AI tools like Codex. This visual, interactive method is often faster and more effective for communicating ideas than writing traditional product specifications.

At OpenAI, the first question is "Can we solve this with the model (tokens) instead of pixels?" This treats the AI as the primary design material, pushing designers to think about interaction and behavior before creating bespoke user interfaces.