Learners demand hands-on experience. The next evolution of training involves AI agents that act as sidekicks, not just explaining concepts but also taking over the user's screen to demonstrate precisely how to perform a task, dramatically accelerating skill acquisition and reducing friction.

The next major evolution in AI will be models that are personalized for specific users or companies and update their knowledge daily from interactions. This contrasts with current monolithic models like ChatGPT, which are static and must store irrelevant information for every user.

CMO Laura Kneebush argues that trying to "get good at AI" is futile because it evolves too quickly. Instead, leaders should focus on building organizations that are "good in a world that's going to constantly change," treating AI as one part of a continuous learning culture.

The best test of knowledge is the ability to teach it. By having employees explain a new AI tool or workflow to their peers, they are forced to solidify their own understanding and identify knowledge gaps. This process turns passive learning into active expertise.

Driving company-wide AI adoption doesn't require massive training programs. Short, consistent, and practical 15-minute weekly sessions showcasing useful applications can create a powerful cultural shift and accelerate learning more effectively than large-scale, infrequent training.

The most valuable data for training enterprise AI is not a company's internal documents, but a recording of the actual work processes people use to create them. The ideal training scenario is for an AI to act like an intern, learning directly from human colleagues, which is far more informative than static knowledge bases.

RAMP discovered that the best way to teach employees AI is through the product itself. The most successful users learned by immediately using a feature and getting a result. This suggests designing AI tools where features act as implicit lessons, teaching best practices during use.

Static data scraped from the web is becoming less central to AI training. The new frontier is "dynamic data," where models learn through trial-and-error in synthetic environments (like solving math problems), effectively creating their own training material via reinforcement learning.

Team members learn the capabilities and best practices for using their own AI agents by observing others' interactions in public channels. This "mid journey dynamic" creates a tacit transmission of knowledge about what's possible, accelerating the entire organization's learning curve much faster than formal training.