In the first week of January, every gym in the country is filled with people who have decided that this is the year they will transform their health. They’ll eat better. They’ll get more sleep. They’ll workout daily. By February, most of these newcomers are nowhere to be seen. Transforming multiple health behaviors at once is really hard for humans unless a major health event like a heart attack or diabetes diagnosis forces them to do so. Consistent, incremental change is not only more sustainable but, considering the setbacks (e.g., injuries) that can arise from making drastic changes, it’s also faster.
The same is true in business. We see this playing out with AI right now, where many companies are caught between two flawed strategies: paralyzing caution, waiting for the technology to be “proven,” and distant moonshots in which massive transformations promise to reinvent entire organizations. Waiting almost guarantees you’ll be left behind by competitors who are already mastering a technology that will create order-of-magnitude shifts in business models. Meanwhile, most research shows that grand transformations fail with frequency. They can consume vast resources—often up to 10% of annual revenue—only to often leave organizations burnt out and distracted.
What if the path forward on AI is not grand transformation, but day-in, day-out honing?
Honing is not as glamorous as a moonshot, but it is no less ambitious. It’s about structuring progress differently: embedding improvement into everyday practice rather than waiting for perfect consensus, breakthrough technology, or flawless infrastructure. And in the end, it’s often faster because it avoids setbacks and costly corrections that come from rushing or making abrupt changes. By steadily aligning with market shifts and making incremental improvements, teams maintain constant momentum and can adjust to insights and advances in real time.
When leaders adopt the honing mindset with AI, it becomes part of organizational daily action rather than an episodic campaign. Instead of a single moonshot, focus on a portfolio of small, targeted experiments that build momentum. Here’s what honing looks like when applied to AI.
These examples share a common thread: they don’t wait for the technology to be settled or the solution to be clear. They build progress through smaller, visible wins that reinforce confidence and accelerate adoption. And all of them rely on a management system which aims for a targeted behavioral outcome.
If you want people to adopt AI, you must change the systems that guide them. These moves won’t stick unless you adjust your company’s management systems—the formal and informal rules that govern an organization. We call management systems the “nervous system” of organizations because they are the things that drive change – or – all too frequently – hold people back from changing.
Here are a few ways that management systems can be shifted to create traction for AI efforts.
When organizations continuously adjust these systems, they embed AI into everyday decision-making. The result can be a culture that restores its edge daily, rather than one that dulls until a major transformation is forced.
The lesson is simple: don’t wait for perfect information or universal buy-in. Leaders should treat AI as a tool to experiment with—testing small-scale applications, monitoring outcomes carefully, and adjusting continuously. Honing can keep AI aligned with an organization’s elemental purpose by forcing constant feedback, assessment, and correction. And if it can work for the adoption of AI, just imagine how many other challenges of the modern organization might be addressed by honing as well.
Stop planning the moonshot. Start honing.
