The future of technology isn't about finding the perfect commercial use case for AI on day one; it's about building the institutional muscle to play the long game. This isn't a new approach to innovation, but rather the very essence of how it has always worked. In a world where everyone from executives to engineers is trying to make sense of the AI revolution, the most strategic move an engineering team can make is to create a culture of deliberate, low-stakes experimentation. Its success is rooted in a simple truth: the most powerful way to make new tech instantly relatable is through personal projects. We’ve seen even the most skeptical engineers become our biggest AI evangelists because of this personal connection.

Instead of demanding an immediate return on every initiative, we're making space for our teams to "play." We actively encourage a healthy mix of professional and side projects, recognizing that the insights gained from solving a personal problem or exploring a new concept are what truly build the skills needed for larger, more complex AI initiatives. The central insight here is that commercial value is a by-product of the process, because as teams increase their competence, they organically start to attempt more complex and commercially relevant problems.

A Culture of Action, Not Just Ideas

This culture of "play" is a company-wide culture. We don't deliberately create this high-energy environment; frankly, after just providing the tools we can't stop our engineers from playing. All we can do is guide that energy towards commercial value. The results speak for themselves: recent reports show that we’ve achieved 89% penetration of AI across our business units.

• Leader-Driven Gauntlets: Our senior leadership regularly lays down challenges for engineers to solve problems that others have long since given up on, such as a complex code generation issue. In one instance, rather than penalize an engineer for a significant inference bill, his leader encouraged him to hold a demo, showcasing his ability to lean into a new way of working.
• Organic Exploration: Engineers, without any direct provocation, have taken it upon themselves to experiment with projects like lobby and test generation, showcasing a proactive curiosity.
• Cross-Functional Collaboration: This mindset even transcends departmental lines, with our engineers being "seconded" by the HR department to build cool apps for talent management. This shows a company-wide recognition that providing freedom to experiment is the fastest way to build expertise. An executive member recently hosted a demo of an impressive process automation he built using Claude code and advanced prompting techniques he learned from one of the developers. This further validates that the skill-building is flowing in all directions.

From Play to Profit

The ultimate goal is always to commercialize AI, and after a period of experimentation, we are finally doing so. The "play" is what builds the muscle, enabling you to accelerate a team's core competency. That increasing competence is what allows for the natural, organic emergence of commercial value.

So stop waiting for a perfect AI strategy. Build the muscle, provide the tools, and get out of the way. The winners will find you.