Mandating AI usage can backfire by creating a threat. A better approach is to create "safe spaces" for exploration. Atlassian runs "AI builders weeks," blocking off synchronous time for cross-functional teams to tinker together. The celebrated outcome is learning, not a finished product, which removes pressure and encourages genuine experimentation.

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.

Webflow accelerates AI tool adoption using company-wide "Builder Days." This combines a top-down executive mandate (e.g., "no meetings without a prototype") with bottoms-up enablement, including tool access, support channels, and prizes. The goal is to move the entire organization up the adoption curve, not just early adopters.

To drive AI adoption, CMO Laura Kneebush avoids appointing a single expert and instead makes experimentation "everybody's job." She encourages her team to start by simply playing with AI for personal productivity and hobbies, lowering the barrier to entry and fostering organic learning.

Snowflake established a cross-functional AI council with volunteers who dedicate 10-20% of their time to experimentation. This avoids chaotic, duplicated efforts from a company-wide mandate. The council then shares learnings and rolls out proven use cases to the broader team quarterly, ensuring structured adoption.

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.

Flexport's internal hackathons are now its primary source for AI-driven innovation. With 90% of projects using LLMs, these events generate real product features and influence the company's roadmap. This demonstrates a powerful bottom-up approach where the most valuable ideas come from engineers closest to the problems.

At Block, the most surprising impact of AI hasn't been on engineers, but on non-technical staff. Teams like enterprise risk management now use AI agents to build their own software tools, compressing weeks of work into hours and bypassing the need to wait for internal engineering teams.

The employees who discover clever AI shortcuts to be 'lazy' are your biggest innovation assets. Instead of letting them hide their methods, companies should find them, make them heroes, and systematically scale their bottom-up productivity hacks across the organization.