Generieke AI-training mislukt om een eenvoudige reden: de uitdagingen van een senior jurist bij het gebruik van AI zijn fundamenteel anders dan die van een junior analist of een operationeel manager. Één programma kan niet aan al deze behoeften voldoen.
The AI training market has responded to massive demand with a predictable solution: scalable, generic programmes that introduce AI concepts, demonstrate tool capabilities, and leave participants to figure out the application to their own work. The result is high training completion rates and low capability change. Most employees who complete a generic AI training programme remain exactly as uncertain about how to use AI in their actual job as they were before. This is not a failure of the participants — it is a failure of the training design.
The Generic Training Problem
Het probleem met generieke training
Generic AI training has three structural weaknesses. First, it teaches AI capabilities rather than job outcomes — participants learn what AI can do in the abstract rather than how to use it for their specific responsibilities. Second, it is passive — participants watch demonstrations rather than practicing with their own work and tools. Third, it is context-free — the examples and exercises use generic business scenarios that require mental translation before they apply to the participant's actual context.
The mental translation required to move from "here is what AI can do generally" to "here is how I should change how I work tomorrow" is significant, and most people don't make it without help. The result is that training investment produces awareness without behaviour change — the worst possible outcome from a capability development perspective.
What Role-Specific Training Looks Like
Hoe rolspecifieke training eruitziet
Effective AI training is built around the specific tasks, tools, and decisions of the participant's actual role. A training for credit analysts is different from a training for logistics planners, which is different from a training for HR business partners. Each programme uses examples from the actual domain, practices on the actual tools the participants will use in their work, and produces outputs that participants can use immediately.
The design process for role-specific training starts with task analysis: what are the ten most important tasks in this role, which of them could benefit from AI assistance, and what specific AI capability would make the biggest difference? The training is then built around those specific tasks, with hands-on practice as the primary learning modality.
Practice-Based Learning: The Critical Element
Praktijkgericht leren: het cruciale element
The research on skill acquisition is clear: people learn by doing, not by watching. AI capability is a skill — it requires practice, feedback, and iteration to develop. The most effective AI training programmes spend the majority of time in active practice: participants work through real tasks with AI assistance, encounter the limitations and failure modes of AI tools, develop judgment about when to trust and when to verify, and build a personal prompt library they can use immediately after training.
The benchmark we use for our training programmes: if a participant can't do something useful with AI in their actual job by the end of the session, the session hasn't worked. This shifts the quality measure from completion to capability — and it requires very different training design.
The Organisational Training Architecture
De organisatorische trainingsarchitectuur
A complete organisational AI training architecture has multiple layers:
- Foundation layer (all staff): AI awareness, ethical use, data security basics, and the organisation's AI policy. Delivered once to all employees, updated annually.
- Role layer (function-specific): AI capability development for specific job functions — the hands-on, practice-based sessions described above. Different programme for each major role type.
- Advanced layer (selected professionals): Advanced AI workflow design, prompt engineering, and AI integration capability for professionals who will lead AI adoption in their teams.
- Leadership layer (managers and senior staff): AI strategy, governance, team AI development, and AI-informed decision-making. Focuses on the management dimensions of AI rather than hands-on tool use.
The investment required to deliver role-specific, practice-based AI training is higher than generic training — but the return is dramatically higher. Organisations that develop genuine AI capability in their workforce see sustained, growing productivity improvements. Those that invest in generic training see training completion metrics improve while actual capability stagnates.
All V&VZ training programmes are role-specific, in-company, and built around the actual tools and tasks of the participants. We design from business outcome backwards: what does your team need to be able to do after this training? If you'd like to explore a training programme designed for your specific context, we'd be glad to discuss.