Exploring the future of learning together

By working in the roles of the Dreamer, the Realist, and the Critic, participants had the opportunity to think visionary, concretize what is possible, and highlight the challenges that must be addressed along the way. It became an appreciated moment that offered a richer picture of how AI is perceived and what organizations are actually asking for.

When we dreamed about the future‍

In the dreaming phase, there was a clear desire to make learning more personal and relevant. Many groups imagined AI agents that understand employees’ strengths, behaviors, and motivations and can provide guidance at the right time.

Several dreams also focused on more inclusive learning where AI can reduce language barriers, reveal potential, and create more equal opportunities. Other ideas touched on automating training materials, learning-style-based content, and more seamless learning experiences in everyday life.

When visions became plans‍

When participants took on the role of the Realist, the discussions became more structured. Here they identified what is actually required to build the solutions envisioned by the Dreamer.

They discussed data quality and master data, technical integrations, well-designed competency models, and the need for a clear framework for privacy and GDPR. Many also emphasized the importance of a culture characterized by trust, openness, and psychological safety for AI to be used meaningfully.

When the ideas were challenged‍

The Critic phase provided space to highlight risks and uncertainties. Questions were raised about data security, ownership, costs, quality, and environmental impact.
Several groups also expressed concern about how AI affects creativity, judgment, and the ability to think independently. Others pointed to organizational challenges such as unclear leadership, limited maturity, or difficulties keeping up with the technology.

What we took with us‍

The workshop clearly showed that interest in AI in learning is high, but that expectations are nuanced. Participants see both the potential and the complexity.
We mainly took away three insights: that the dreams exist, that the path there requires both structure and culture, and that responsible use is essential for AI to create real value.

It also became clear that the need for more personalized, engaging, and inclusive learning is great, and that AI can play an important role if built on the right foundation.‍

Summary of the key insights

1. A strong desire for hyper-personalized learning
   AI should understand the individual, provide relevant recommendations, and offer continuous support.
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2. AI as a tool for inclusion
   Participants saw great opportunities to reduce language barriers, increase fairness, and reveal potential.
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3. Data is the critical prerequisite
   Without structured and reliable data, all major AI initiatives fall apart.
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4. Culture and leadership are central
   Trust, safety, and transparency must be present before AI can create real value.
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5. Integrity and security are the greatest concerns
   GDPR, data protection, ownership, and ethical considerations were mentioned in almost every group.
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6. The balance between humans and AI
   Participants want AI that enhances the human, not replaces it.
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7. Automation is appealing, but quality must be ensured
   AI can create materials and training content, but requires human oversight.
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8. Learning is increasingly seen as an ecosystem
   The vision is seamless, integrated learning where AI connects needs, activities, and development over time.