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.

Allocate resources strategically to ensure both short-term stability and long-term innovation. Dedicate 70% of effort to the core business (1-2 year impact), 20% to riskier medium-term bets (3-5 years), and 10% to high-risk moonshots.

Beyond a certain salary, top engineers are driven by creative purpose, not just compensation. Excel Data retains talent by encouraging engineer-led initiatives, such as building their own open-source data platform (ODP) or AI vulnerability-fixing agents, which fosters a culture of meaningful innovation.

In ROI-focused cultures like financial services, protect innovation by dedicating a formal budget (e.g., 20% of team bandwidth) to experiments. These initiatives are explicitly exempt from the rigorous ROI calculations applied to the rest of the roadmap, which fosters necessary risk-taking.

The most successful companies deploying AI use a "leadership lab and crowd" model. Leadership provides clear direction, while the entire organization is given access to tools to experiment and discover novel use cases. An internal team then harvests these grassroots ideas for strategic implementation.

Google's early, unstructured engineering culture allowed employees like Noam Shazir to pursue contrarian ideas like language models without direct management. This freedom directly led to foundational products like spell check and the core technology behind AdSense, demonstrating how autonomy can fuel breakthrough innovation.

Instead of a linear handoff, Google fosters a continuous loop where real-world problems inspire research, which is then applied to products. This application, in turn, generates the next set of research questions, creating a self-reinforcing cycle that accelerates breakthroughs.

Siphoning off cutting-edge work to a separate 'labs' group demotivates core teams and disconnects innovation from those who own the customer. Instead, foster 'innovating teams' by making innovation the responsibility of the core product teams themselves.

Products like video generator Flow and research tool NotebookLM are not built in a vacuum. Google Labs actively seeks input from creatives like filmmakers and authors to shape experimental AI tools, ensuring they solve real-world problems for non-technical users from the start.