AI needs more than technology — it needs people
A workshop report from LEARNTEC 2025 — from the perspective of Miriam Mertens, Co-Founder & CEO of DeepSkill
Every year, LEARNTEC is a fixed point for everyone who is concerned with the future of learning. This time I was not only able to give a presentation, but also to accompany an intensive workshop with experts from a wide range of industries — and rarely has the exchange been so honest, practical and to the point at the same time.
The focus was on a question that concerns us all: Why is AI enablement failing in so many organizations — and what does it take to make it work?
When AI Enablement Fails, It’s Rarely Because of the Technology
During an inspiring workshop on “Building AI Competence,” we discussed one of the biggest obstacles to successful AI transformation with experts from the telecommunications, IT, and automotive supply sectors: fragmented responsibilities.
Whether it’s IT, strategy, HR, or AI innovation teams – AI enablement involves many stakeholders, but often lacks a shared table. The result: good ideas fizzle out, use cases stay siloed, and potential goes untapped. This was exactly the starting point for our workshop – which led to an unexpectedly lively and honest discussion about how organizations can unlock AI’s potential by working across departmental boundaries.
Insights from the Field: One Challenge, Many Perspectives
Participants from various industries shared similar struggles: lack of coordination between departments, uncertainty in dealing with new technologies, and the question of who is actually “responsible” for AI strategy, skill-building, and implementation.
What stood out: this pattern exists across companies of all sizes and maturity levels. Many organizations are technologically ready – but lack cross-functional coordination and targeted skill development that focuses not only on tools but on people. As one participant put it:
“It’s not enough for IT to understand what an LLM can do – HR also needs to know how to prepare employees for it.”
Key learnings from the workshop
Four key findings emerged from the intensive discussions and presentation content:
- AI transformation is a strategic HR responsibility:
Without targeted skill-building, AI remains an expensive tool. With the right competencies, it becomes a gamechanger.
- 85% of AI projects fail due to lack of employee know-how:
To avoid this, organizations need role-based upskilling aligned with their business context.
- Uncertainty is the biggest innovation blocker:
People use AI 30–40% more effectively when they understand how it works – and what its limitations are.
- Widespread AI understanding is key to innovation:
Only when employees grasp how AI functions in their business context can they contribute meaningful ideas.
The Bottom Line: Assess Together, Act Together
The consensus of the workshop: a structured, solution-oriented approach is essential. Only when all relevant stakeholders evaluate opportunities and risks together can realistic use cases be defined and tested in pilot projects – whether for process automation or for developing innovative, data-driven products.At DeepSkill, we see AI enablement not as a technical project, but as a strategic transformation journey. Our modular AI learning program starts here: combining foundational knowledge, ethical guidelines, and hands-on use cases – all aligned with roles and business goals.
Let's grow deep — especially when it comes to AI.
This workshop was a strong signal: organizations are ready to shape change, but they need new forms of collaboration and empowering learning solutions. That’s exactly what DeepSkill offers – with a methodical approach that puts people at the center and integrates cultural change.If you’re ready to unlock AI’s potential in your company: let’s talk. For a future where AI connects – not divides.
