When people talk about AI, they usually think about technology—algorithms and machine learning models. But the real change AI brings is in how it forces us to rethink our organizations. Look closely at forward-thinking companies and you'll notice a pattern: they're all grappling with how AI will reshape not just their technology, but their entire way of working.

Engineers, by nature of their roles, are deeply immersed in product development. They focus on enhancing features, improving user experiences, and pushing the boundaries of what their products can do. This focus, while crucial for innovation, often creates a blind spot: they miss the myriad of operational challenges faced by other departments.

To bridge this gap, some companies have introduced a new role: the AI "general-purpose athlete." These aren't traditional PhD engineers in machine learning. These are employees that are curious about new tools, can understand and evaluate external technologies, and are comfortable building simple custom solutions on top of existing AI platforms such as OpenAI. Interestingly, these leaders are most successful when they sit outside of the engineering department.

This role represents a significant shift in organizational thinking. A few years ago, hiring an employee or small team to automate different areas of the business would have seemed impractical. Such tasks were typically outsourced to consultancies, leveraging their broad expertise and resources but requiring expensive implementations. However, the landscape has changed dramatically. Technological democratization has altered the unit economics, making it increasingly feasible and favorable for companies to bring much of this capability in-house.

Consider a $250M revenue online marketplace. The CEO calls these AI athletes his "efficiency hackers"—a small team tasked with evaluating efficiency opportunities across all departments. Their approach is simple: ask each department to describe their three most time-consuming tasks, then figure out if these can be automated with an off-the-shelf tool or a simple custom solution.

One concrete example from this company involved the procurement team. They were spending hours manually converting complicated pricing data from PDF documents to Excel, and then reformatting the data to fit their custom database. The AI team built a tool that automated this process, freeing up the procurement team to focus on more valuable work—such as interacting with customers, something AI can't do (yet).

However, the introduction of AI athletes is not without its challenges. The most significant hurdle is often not technological but cultural. Getting business teams invested in the success of these new tools can be an uphill battle. After all, change is hard, especially when it means your day-to-day work will significantly change or, worse, you may be worried about job security. This points to a broader shift in the talent profile required for leadership roles in the AI era.

Companies that adjust their metrics and incentive structures to reward AI adoption are likely to move faster. Those embracing the AI athlete strategy report that they are outpacing their competitors and achieving remarkable productivity gains. We are at the beginning of experiencing firsthand examples of how integrating AI into an organization's DNA can be a game-changer.