To truly integrate AI, go beyond simply telling your team to "learn more." The founder of Search Atlas advocates for organizing multi-day, in-person hackathons. This focused, collaborative environment, where teams tackle specific problems together, fosters a deeper and faster mastery of practical AI applications than solo, online efforts can achieve.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Harvey's Forward Deployed Engineering team isn't just for building custom solutions. It's a strategic product discovery tool. By embedding engineers with large clients who have undefined GenAI needs, Harvey identifies and builds the next set of platform features, effectively using customer problems to pave its future roadmap.

Flexport's founder details his journey from a hands-off "manager mode" to a directive "founder mode." The rise of bottom-up AI innovation in hackathons is causing him to cycle back, recognizing the need to balance top-down strategy with empowering creative, decentralized ideas that leadership couldn't have conceived.

High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.

Amplitude's CEO transformed his organization not by issuing a product roadmap, but by first focusing on internal education. An "AI week" and hackathons got the engineering team using AI tools like Cursor, building belief and capability before they were tasked with creating new AI features.

Stripe's Experimental Projects Team discovered that embedding its members directly within existing product and infrastructure teams leads to higher success rates. These "embedded projects" are more likely to reach escape velocity and be successfully adopted by the business, contrasting with the common model of an isolated R&D or innovation lab.

When an engineering team is hesitant about a new feature due to unfamiliarity (e.g., mobile development), a product leader can use AI tools to build a functional prototype. This proves feasibility and shifts the conversation from a deadlock to a collaborative discussion about productionizing the code.

To transform a product organization, first provide universal access to AI tools. Second, support teams with training and 'builder days' led by internal champions. Finally, embed AI proficiency into career ladders to create lasting incentives and institutionalize the change.