During the initial week of January, fitness centers nationwide experience a surge of individuals committed to improving their health this year. Their diet will improve. They're going to sleep more. They'll exercise every day. By February, the majority of these new arrivals have vanished. It's exceptionally difficult for people to change several health habits simultaneously, unless a significant health crisis, such as experiencing a heart attack or being diagnosed with diabetes, compels them to do so. Gradual, sustained adjustments are not only more enduring but, given the potential for setbacks like injuries when making abrupt shifts, they also lead to quicker progress.
TL;DR
- Companies should focus on gradual, ongoing AI enhancements, not grand, risky transformations.
- Integrate AI into daily operations through small, focused experiments and process improvements.
- Modify management systems to encourage AI adoption, learning, and continuous adaptation.
This principle also applies to the business world. 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 have the potential to completely reshape organizations. Delaying adoption of a technology that promises significant business model transformations will likely result in competitors outpacing you as they gain mastery. However, the majority of studies indicate that significant changes often don't succeed. 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?
The power of honing
Our new book Hone: How Purposeful Leaders Defy Drift contends that companies ought to transition from depending on infrequent, extensive overhauls to ongoing, deliberate minor changes. While transformations are sometimes needed, what we refer to as “honing”— subtle yet intentional adjustments that foster progressive momentum — is largely overlooked. Organizations can refine their AI strategies proactively, much like a chef sharpens a knife daily to maintain its edge, thus avoiding the more damaging process of extensive repair when it becomes dull. Robust, and ultimately quicker and more impactful than transformation.
While not as flashy as a moonshot, honing is an equally ambitious endeavor. The focus is on a different approach to structuring progress: integrating enhancements into daily routines instead of delaying until ideal agreement, groundbreaking technology, or faultless infrastructure is achieved. Ultimately, this approach frequently proves quicker by sidestepping delays and expensive fixes that can arise from haste or sudden alterations. Teams maintain consistent progress and can adapt to new information and developments as they happen by consistently adapting to market changes and making gradual enhancements.
When leaders embrace the honing mindset with AI, it integrates into an organization's everyday operations, moving beyond isolated initiatives. Rather than one grand endeavor, concentrate on a collection of modest, focused trials that generate progress. This is what AI refinement entails.
- Enhance current processes prior to pursuing complete automation. For many organizations, simply enhancing current processes with AI—rather than attempting to replace them wholesale—can unlock immediate value. For sectors such as customer service or supply chain operations, this might involve integrating AI into current systems to optimize processes, enhance human judgment, or boost prediction precision. While these actions might not yield significant changes immediately, they foster competence, confidence, and forward movement. Furthermore, employing AI fosters learning that can be applied in other contexts.
- Make “minimally viable moves”. Applied to AI, this means breaking big challenges into approachable experiments. A company could begin by employing machine learning to fine-tune inventory for a single product category, rather than attempting to integrate AI throughout its entire supply chain. Instead of automating every customer interaction, a team might test a chatbot for a particular service area and assess its performance. An organization could trial an AI-powered forecasting tool in one area before implementing it across the entire company, even at the operational stage.
- Don’t wait for the next iteration of the model. The push for applying AI often gets bogged down in debates about how long it will take to achieve artificial general intelligence (AGI) or what the next set of models will bring. Although anticipating future developments is beneficial, you'll generally be better equipped for what lies ahead by honing your skills with current tools rather than holding out for improved future iterations. Future adaptations are seldom hindered by current actions. Companies can establish strong machine learning operations, model interpretability guidelines, and ethical AI protocols that adapt as technology advances.
- Develop a framework that encourages ongoing improvement. Teams working with AI should feel simultaneously like it’s not optional to work with the technology in some way, while also not feeling paralyzed by the need to for it to be perfect. Rewards ought to encourage uptake and, crucially, not penalize “failure.”. Ideally, we'd prefer to stop using the term “fail fast” altogether. Everyone dislikes failure; therefore, incentives ought to acknowledge teams that adopt and learn the technology. As the organization gains knowledge, standards and expectations ought to be consistently elevated.
These examples share a common thread: they don’t wait for the technology to be settled or the solution to be clear. Progress is built via incremental, observable successes that bolster confidence and speed up adoption. Each of these depends on a management system designed to achieve a specific behavioral result.
To encourage AI adoption, it's essential to modify the systems that direct people. These changes won't endure unless your company's management systems, which are the established and unwritten principles guiding an organization, are modified. 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.
Management systems can be altered in several ways to foster progress in AI initiatives.
- Decision rights: It may be necessary to have some degree of central control over the portfolio of tests that an organization is undertaking in AI. Taking a “let a thousand flowers bloom” approach by decentralizing testing could make it harder to share the learning of initial pilots and speed up, forcing each part of the organization to create their own journey.
- Performance evaluation: Add the adoption of AI to goals; just be cautious of what is measured – if it’s the success of an early test, it could inadvertently put a ceiling on ambition.
- Budgets: Leadership can allocate some flexible funds that allow teams to test and scale AI ideas quickly, rather than tying them to multi-year capital projects.
- Meeting norms: We have seen some teams adopt an “AI Moment” in regular meetings where teammates share what they’ve learned. This integrates experimentation into the norm, establishing AI as a cultural element rather than a distinct initiative.
By consistently modifying these systems, companies integrate AI into their daily choices. The result can be a culture that restores its edge daily, rather than one that dulls until a major transformation is forced.
The takeaway is straightforward: avoid delaying until you have complete data or everyone agrees. Leaders should treat AI as a tool to experiment with—testing small-scale applications, monitoring outcomes carefully, and adjusting continuously. Continuous refinement can ensure AI remains consistent with a company's core mission through ongoing input, evaluation, and adjustment. If AI adoption can be facilitated this way, consider the potential for addressing numerous other modern organizational challenges through similar improvements.
Stop planning the moonshot. Start honing.
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