Learning to learn: train talent or self-manage with AI?

Learning to learn: is it necessary to continue training talent or can they self-manage with AI?

The impact of generative artificial intelligence on corporate training. Is it necessary to continue training talent or can they self-manage with AI?

In a world where everything changes at high speed, the capacity to learn is more valuable than any specific knowledge. Therefore, training today is conceived in a more intentional, contextual, and flexible way, adapting to what each person and each team needs, according to their current moment and their challenges.

In other words, generalist models are transforming into more personalized and individual schemes, connected to the business strategy.

How companies train today

Current training focuses range from specialized technical capabilities, which are constantly evolving, to adaptive and collaborative skills, such as critical thinking, continuous learning, communication, and change management.

Beyond the technical side, which changes all the time, there are capabilities that are becoming structural: learning to learn, systems thinking, self-management, and accountability are just a few of them. We must also not forget the importance of adaptability in increasingly uncertain contexts and the ability to collaborate in distributed and multicultural environments, typical of an increasingly globalized reality.

Critical thinking, for its part, remains essential for being able to question, analyze, and face challenges. And, of course, it’s not just about knowing how to use AI, but about understanding its logic and applicability—that is the moment when it becomes truly powerful.

Is training still necessary?

In this sense, there is no doubt that the intelligent use of GenAI tools—both to boost productivity and to enrich creative or problem-solving processes—is positioned as a powerful ally.

However, despite any speculation that may arise, GenAI does not replace training: it redirects it. It becomes part of the toolbox, and knowing how to leverage it becomes a cross-functional capability.

That is why companies today value traits in any talent such as curiosity, autonomy, critical thinking, and a commitment to continuous learning. In a context where roles transform at high speed, priority is given to flexible individuals who dare to explore, ask, test, challenge, and learn alongside others. AI may change the "hows," but it does not replace those attitudes.

Beyond AI

In the same vein, besides AI, there are other types of technologies impacting new ways of training.

• Automation and low-code/no-code: For example, these can be useful for redefining the tasks performed by each role.
• Collaborative technologies: These facilitate distributed environments and asynchronous work.
• Intelligent digital training platforms: Ideal for creating personalized learning itineraries.
• Augmented or Virtual Reality (AR/VR): Can be highly useful when applied to technical training or simulations for certain profiles.

These innovations do not just redefine what we need to learn, but also how, when, and with what experience. In fact, AI and automation tools are accelerating learning times, lowering the barrier to entry for new knowledge, and allowing skill development to be more guided, agile, and accessible.

Faced with this scenario, designing training with purpose will be one of the clearest ways to ensure business sustainability and competitiveness. Training with focus doesn't just develop talent; it also generates organizational agility, commitment, and responsiveness. Conversely, failing to invest—or doing it poorly—means risking falling behind in a constantly evolving market, losing key people, or being unable to meet upcoming challenges. Ultimately, learning is not just a business area: it is the engine that drives it toward the future.