The Illusion of Progress: Why the Learning Industry Doesn't Understand Learning

Author: Viktor Vetturelli, COO at qohubs

A month ago, LEARNTEC wrapped up in Karlsruhe. At Europe's largest trade fair and conference on digital learning—more than 400 exhibitors, 12,000 visitors across three days—the future of learning was on display.

The fair fell roughly into three categories: skills development, language content, and AI coaching with conversational simulation. At various stages, companies such as the VW Group Academy and Bayer AG shared their best practices. As the only Croatian company tangentially represented in this category, we had the advantage of being the outsider, the observer.

What else was objectively noticeable? AI coaching and conversational simulation continued to grow. Virtual and augmented reality exhibitors, unlike previous years, became secondary. Everything revolved around AI. But what I didn't see — not at a single booth, not on a single stage — was engagement with these questions:

• What is relevant knowledge today?

• How does it come into being?

• How do we know knowledge has formed?

• Which skills are now relevant?

• How does a company become intellectually smarter?

Instead, everywhere one looked, in every conceivable color, came AI coaching, conversational simulation, and auto-generated learning pathways. The message was uniform: effortless development, competencies on demand, future-readiness as a mindset problem. Faster. Increasingly personalized. More efficient. Comfortable for learning leaders and learners, too. But here's the catch: this isn't learning — it's the avoidance of learning through the promise of better tools.

Where Does the Real Problem Lie?

"Let anyone who thoughtlessly grasps at scientific and technical miracles, and understands them as little as the cow understands the botany of the grass it happily pastures, be ashamed." — Albert Einstein, still in the last century.

When we speak of learning and development in organizations, we must also speak of how those who learn are treated. AI takes on everything linear and repetitive. It should serve us. What it cannot do — and here humans come into play — is making decisions under uncertainty.

The learning industry seeks to escape this uncertainty by promising that the future will be controllable through skills development. It begins with human deficits and wants, aiming as painlessly as possible to minimize them. Beyond that, not working, we cannot accept the image of humans as a problem that the L&D industry offers us. We must go deeper and think about knowledge itself and what it truly is.

Knowledge is not an object we can possess. Knowledge is capacity, and like any capacity, it emerges through practice in particular contexts and in interaction between people — through observation, interpretation, action, and feedback. Knowledge is always contextual, situational, and provisional. This is precisely why "best practices" often become a path to disaster: they trap us in linearity. "It worked for them, so it will work for us." Peter Drucker and Edwards Deming warned us about this half a century ago.

Are We Enabling It, or Still Preventing It?

Growing uncertainty is a consequence of today's complexity. The "if-then" logic no longer works. Today, we face problems we have never encountered before. These problems are often interconnected and interact with one another. It's no longer enough to solve a problem; the skill is first to extract from that mesh of problems and define what the actual problem is. For that to work, we need different perspectives and contexts from colleagues. AI, as it's currently implemented, prevents exactly this: it seeks solutions within a given frame, and, in terms of complexity, that frame must be continuously redefined.

The response to complexity is not faster learning. It is not more knowledge in the sense of more content, more modules, more AI-generated pathways. The response is becoming better at thinking about context and reality together.

Shared Capacity for Action

And here lies the true betrayal of the industry: it has abandoned critical dialogue and replaced it with training, coaching, and simulation. That laborious, slow, sometimes uncomfortable conversation in which knowledge is not transferred but created. In which one perspective meets another, and something emerges that neither side could think alone.

What organizations urgently need today is not an expert explaining what they should know — AI could do that. They need shared capacity for action. And it emerges in a structure that enables them to discover what they already know and what, together, they can further think and do. This means: the potential for knowledge already exists, but knowledge emerges only through relationships among people. Therefore, organizations don't primarily lack information—they lack conditions in which experience and different perspectives can become a shared understanding of the organizational context.

A learning industry that understands this would not ask how to accelerate learning. It would ask how to enable conversations that make a difference. Therefore, at the end of the day, this is not a question of L&D—it is a question of organizational intelligence.

If you want to prepare your leadership team to deal with complexity and complex questions, drop us a line via meet@qohubs.com or contact us to schedule an Experience Session now.

*This article was written by Viktor Vetturelli and first published in May 2026 on the Poslovni Dnevnik portal.