To overcome employee resistance to learning AI, position it as a personal career investment. Ask them to consider what skills will be required in job interviews in two or three years. This shifts motivation from a top-down mandate to a valuable opportunity for personal and professional growth.

Companies once hired siloed 'digital experts,' a role that became obsolete as digital skills became universal. To avoid repeating this with AI, integrate technologists into current teams and upskill existing members rather than creating an isolated AI function that will fail to scale.

Anticipating that AI will automate baseline work of junior analysts, Temasek’s strategy is to push these employees to develop skills and perform at a level two grades above their current role. This preemptively adapts their talent development model for an AI-enabled world, focusing on higher-order thinking from day one.

To ensure AI adoption is a core competency, formally integrate it into your team's operating system. Webflow is redoing its career ladder to make AI fluency a requirement for advancement, expecting team members not just to use tools but to lead, own, and push the boundaries of AI in their work.

When building core AI technology, prioritize hiring 'AI-native' recent graduates over seasoned veterans. These individuals often possess a fearless execution mindset and a foundational understanding of new paradigms that is critical for building from the ground up, countering the traditional wisdom of hiring for experience.

To build an AI-native team, shift the hiring process from reviewing resumes to evaluating portfolios of work. Ask candidates to demonstrate what they've built with AI, their favorite prompt techniques, and apps they wish they could create. This reveals practical skill over credentialism.

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.

For large, traditional companies, the most critical first step in AI adoption isn't building tools, but fostering deep understanding. Provide teams sandboxed access to AI models and company data, allowing them to build intuition about capabilities before crafting strategy.

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.