Artificial intelligence (AI) is no longer a futuristic idea—it’s already here, and it’s changing how we work. However, there’s a catch: Many leaders think adopting AI is a onetime event, like training users on a new software system. The reality is far different: AI only works if you know how to use it properly, and the instructions aren’t straightforward. You have to learn through doing over and over again.

Sound frustrating? Maybe at first, but the payoff is worth it. And this is where many leaders stumble. AI adoption is a journey that requires deliberate practice, constant learning, hands-on experimentation, and ongoing adaptation. If leaders want their teams to stay competitive and ready for what’s ahead, a culture of continuous learning is essential.

Generative AI is incredibly powerful, but it’s not a quick fix. It’s easy to get excited about AI tools that seem to do everything at the click of a button. But here’s the reality: When it comes to actually integrating AI into your business, things might get worse before they get better. Economists call this the Productivity J-Curve. In the beginning, productivity can actually dip. This happens because employees need time to retrain, workflows must be redesigned, and organizations must adapt to entirely new ways of working.

This isn’t new. In 2002, economists Bas Jacobs and Richard Nahuis looked at how major technologies like electricity and the internet impacted businesses. They found something interesting: highly skilled workers initially spend their time learning and adapting to the new tools, which leads to a short-term productivity slowdown. The same holds true for AI—time and effort are needed up front before achieving real productivity gains.

Goldman Sachs predicts AI could boost global GDP by 7% over the next decade, but that won’t happen overnight. Stanford’s Erik Brynjolfsson has shown that the real benefits of AI come only after businesses invest in continuous learning and make the necessary changes to truly embed these technologies into their operations.

The payoff will come, but it takes time—and the leaders who understand this are the ones who will really see AI’s transformative potential.

The need for continuous learning isn’t limited to mastery of AI or even technical skills. Slalom’s AI research report shows a big shift in the skills that leaders value. The most important skills are no longer technical. In fact, software development fell out of the top five for future-ready organizations.

What matters now? Human skills: critical thinking, creativity, problem-solving, and emotional intelligence. AI can’t replicate these, and they’re exactly what will keep your workforce ahead of the curve.

But there’s a gap. While 82% of businesses plan to increase AI investment in 2025, 42% of companies say a lack of skills is blocking progress. We need to get serious about continuous learning—not just for employees, but for leaders as well.

Leaders themselves play a pivotal role in this shift toward continuous learning. In an era where AI can process vast amounts of information in seconds, human leadership isn’t diminished—it’s elevated. Leaders who embrace a learning mindset set the stage for their organizations to evolve alongside technology.

The role of leadership in AI adoption extends beyond strategy. Leaders must model what it means to learn, experiment, and be curious.

As AI strategist and educator at the Wharton School Ethan Mollick shares, “Most C-level folks (the vast majority of executives I talk to) still haven’t even tried LLMs. Changing that should be the highest priority of anyone who wants their company to succeed at AI.”

Setting aside time to “play” with AI tools, as suggested by experts like Mollick, ensures that leaders not only understand the technology but also discover innovative ways to apply it within their organization. A simple exercise like spending 10 hours exploring generative AI can reveal how these tools can streamline workflows, enhance creativity, and foster new capabilities.

To maximize AI’s potential, you need to embed continuous learning into your organization. AI will only be as effective as the people who use it, and those people need to practice, fail, learn, and repeat. Below is our framework for getting started:

1. AI learning academy: Start with structured learning. Get your teams trained on AI tools but also on critical thinking and adaptability.
2. Gamified experimentation: Encourage your people to try AI in small, low-risk tasks. Gamification can help reduce the fear of failure and promote creative problem-solving.
3. AI coaching: Equip your teams with the skills to mentor each other. Peer learning accelerates adoption and creates a culture of shared knowledge.
4. Real-world application: Give your team practical use cases to create and apply prompts. Whether it’s improving daily workflows or tackling specific business challenges, the real value of AI emerges through applied experience.
5. Adaptive and continuous learning: Build a culture where learning never stops. This isn’t about playing with AI here and there. It’s about encouraging a deep integration of AI into your team’s workflows—so that learning, testing, and applying become second nature.

By integrating these steps, organizations can ensure that they’re not just ”tinkering” with AI, as 79% of organizations admit to doing in Slalom’s study. Instead, they can create a robust learning foundation that fosters sustained growth, innovation, and productivity.