Many companies are accelerating their investment in tech infrastructure, but most aren’t pairing that progress with a clear strategy to build the human capabilities needed to actually leverage it.
According to Deloitte’s State of Generative AI in the Enterprise 2024, 78% of organizations plan to increase their AI investment this year. Yet talent readiness remains low, showing that the pace of technological change is outstripping people’s ability to adapt.
Similarly, IBM’s AI Readiness Index 2025 reveals that the biggest barrier to AI adoption isn’t the tech; it’s the people. Lack of skills and readiness is one of the main obstacles to adopting and scaling AI in organizations, according to the study.

This gap between technology and talent explains why so many AI initiatives end up focusing on big training programs… without real impact on day-to-day work.

‍

‍The illusion of progress: more courses, same results

In many organizations, “adopting AI” still means launching learning paths, producing new e-learning, offering certifications, etc.

The metrics look great: high engagement, high completion rates, positive feedback. But none of that seems to translate into meaningful changes in how people work.

People learn about AI -concepts, tools, features-, but that doesn’t mean they’re learning with AI. The first expands knowledge; the second transforms practice.

What’s missing isn’t content. What’s missing is context, purpose, and room to apply what they learn in real situations.

‍

Learning with AI: from courses to daily work

Learning with AI doesn’t depend on the format. It can start in an online course, in a team meeting, or an informal chat. What matters is integrating learning into the way work actually happens.

When an analyst uses AI to generate hypotheses, an operations specialist tests scenarios with it, or a team applies it to rethink processes, learning stops being an event and becomes a continuous way of working.

That kind of learning requires much more than technical skills: it needs a culture that legitimizes exploration and experimentation instead of prioritizing control and efficiency above everything else.

‍

Adoption isn’t just technological, it’s cultural

‍

Adopting AI isn’t about adding tools; it’s about rethinking how decisions are made, how value is created, and how people interact with technology.

Where the tech is available but beliefs and habits stay the same, the result is predictable: organizations automate what already exists instead of transforming it.

The real frontier is unlearning inherited practices, rigid processes, hierarchical decisions, and the old idea that learning happens outside of work.

Leading learning by making it visible

‍

A culture that learns with AI needs leadership. Not leadership as in “master every tool”, but leadership as in modeling learning: asking questions, showing uncertainty, sharing the process, and creating space for others to experiment without fear of mistakes.

Leading today means creating the conditions for exploration and growth.

‍

From optimizing the old to redesigning human-AI collaboration

‍

Using AI just to speed up what already exists, without questioning the purpose, has limited value. Real transformation demands rethinking how work is organized, how information is interpreted, and how decisions are made with technology.
That shift requires four cultural movements:

1. From teaching skills → to enabling human–AI collaboration.
   It’s not about using the tool; it’s about thinking with the tool.
2. From static content → to knowledge that evolves in practice.
   Learning becomes alive, rooted in real work.
3. From counting training hours → to observing capability growth.
   What matters isn’t how many people are trained, but how their work changes.
4. From long planning cycles → to continuous experimentation.
   The challenge isn’t moving fast, it’s moving differently.

Conclusion

‍

The real question isn’t how many people complete AI training, but how many use AI to transform the way they work.

Adopting AI means redesigning how we learn, decide, and collaborate.

That change doesn’t begin in a learning platform, it starts in daily practice, guided by shared purpose and space to experiment.

Transformation happens when organizations learn with AI, not just about it.

‍