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.

Review your organization's incentive structure for AI. Are employees only rewarded for executing known use cases faster, or are they encouraged to experiment and share lessons? Without explicit rewards for exploration, companies risk stifling innovation and missing out on transformative AI applications that come from experimentation.

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.

AI agent platforms are typically priced by usage, not seats, making initial costs low. Instead of a top-down mandate for one tool, leaders should encourage teams to expense and experiment with several options. The best solution for the team will emerge organically through use.

To overcome inertia and build confidence, leaders should give every person on their team a specific task to complete using an AI tool. This hands-on, mandated experimentation is more effective than broad directives, as it accelerates learning, builds momentum, and demystifies the technology across the organization.

Organizations fail when they push teams directly into using AI for business outcomes ("architect mode"). Instead, they must first provide dedicated time and resources for unstructured play ("sandbox mode"). This experimentation phase is essential for building the skills and comfort needed to apply AI effectively to strategic goals.

The most effective way to integrate AI is not through individual training but by empowering teams to redesign their own work processes. This team-level approach fosters agency and ensures AI is used to solve real, shared problems, which is more powerful than simply making individuals 'AI literate'.

Many companies try automating massive, multi-team processes from day one. A better strategy is to first empower individual employees to build their own agents, fostering a culture of innovation before tackling complex, cross-functional automation.

A key sign of successful AI adoption isn't a reduced workload, but an increase in the team's ambition and capacity for experimentation. By lowering the cost and time of innovation, AI empowers teams to generate and test more ideas, which is a more valuable outcome than simply doing the same work faster.