To lead with AI, start smaller

Sharpening power

In our new book Hoon: How Purposeful Leaders Defy DriftWe believe that organizations must shift from relying on cyclical, comprehensive reinventions to ongoing, targeted micro-adjustments. Shifts may sometimes be necessary, but what we call “sharpening”—making small but intentional changes that build cumulative momentum—is largely underutilized. Just as a chef sharpens a knife daily to keep it in good condition — rather than waiting until it becomes dull and requires a destructive sharpening process — organizations can hone their approach to AI in ways that are less risky, more agile, and ultimately faster and more effective than transformation.

Honing is not the magic of success, but it is no less ambitious. It’s about structuring progress differently: integrating improvement into everyday practices rather than waiting for the perfect consensus, advanced technology, or flawless infrastructure. Ultimately, it is often faster because it avoids setbacks and costly corrections that come from rushing or making sudden changes. By steadily aligning with market shifts and making incremental improvements, teams maintain sustained momentum and can adapt to insights and progress in real-time.

When leaders adopt the mindset of honing with AI, it becomes part of daily organizational work rather than an occasional campaign. Instead of achieving a single accomplishment, focus on a series of small, targeted experiences that build momentum. Here’s what sharpening looks like when applied to AI.

• Optimize existing systems before aiming for full automation. For many organizations, simply enhancing existing processes with AI – rather than trying to replace them wholesale – can unlock immediate value. In industries such as customer service or supply chain management, this may mean integrating AI into existing platforms to streamline workflow, augment human decision-making, or improve forecasting accuracy. These steps may not lead to radical transformation overnight, but they work to build capacity, confidence, and momentum. Most importantly, the practice of using AI creates learning to apply elsewhere.
• Make “minimum viable moves.” Applied to AI, this means turning big challenges into accessible experiences. Instead of trying to implement AI across the entire supply chain, a company might start using machine learning to optimize inventory for just one production line. Instead of trying to automate all customer interactions, a team can pilot a chatbot for a specific service category and evaluate its effectiveness. Even at an operational level, an organization may pilot an AI forecasting tool in one region before scaling it company-wide.
• Don’t wait for the next iteration of the model. Efforts to implement AI often get bogged down in discussions about how long it will take to achieve artificial general intelligence (AGI) or what the next set of models will bring. Although it’s helpful to have an idea of ​​what’s to come, you’re always better prepared for the future by practicing with the tools that exist today rather than waiting for the next versions to be better. Today’s moves rarely get in the way of future adaptations. Organizations can build robust practices for machine learning processes, model interpretive standards, and ethical checklists for AI that can evolve alongside the technology.
• Design a system that promotes continuous progress. Teams working with AI must simultaneously feel that it is not optional to work with the technology in some way, while also not feeling paralyzed by the need to be perfect. Incentives should reward adoption, not specifically punish “failure.” In fact, we’d rather never use the term “fail fast” again. No human being likes failure; Incentives should reward teams that use technology and learn. Standards and expectations should be continually raised over time as the organization learns.

These examples share a common thread: they don’t wait until the technology stabilizes or the solution becomes obvious. They build progress through smaller, clear wins that strengthen trust and accelerate adoption. They all depend on a management system aimed at achieving a targeted behavioral result.

If you want people to embrace AI, you have to change the systems that guide them. These moves will not last unless you adjust your company’s steps Management systems-Formal and informal rules that govern the organization. We call management systems the “nervous system” of organizations because they are the things that drive change or, more often, prevent people from changing.

Here are some ways management systems can be transformed to attract AI efforts.