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Training

AI training & workshops

From awareness workshop to setup — enabling teams to use safely and productively.

Most employees have already tried — usually privately, often with free cloud tools, almost always without training. The result is : data lands in third-party cloud services, hallucinations get mistaken for truth, and no one knows which tools the team is actually using.

Since 2 February 2026, under Art. 4 of the is mandatory: companies deploying must prove their staff understand the systems they use — how they work, their limits, the risks. Training is no longer nice-to-have, it is a regulatory requirement.

Good training acknowledges two realities: management needs different content than a case worker, and a half-day event without follow-up changes nothing in how people work. So we don't build a standard programme but a tiered curriculum with multipliers and update discipline.

Process5 steps at a glance

  1. Audience analysis

    Roles, prior knowledge, existing tools, industry duties — define learning goals per audience.

  2. Curriculum design

    Modular library (fundamentals, , tools, data protection, ), three depth tiers, build plan to fit.

  3. Delivery

    Hands-on in a environment, own data or anonymised examples, on-site / remote / hybrid.

  4. Train multipliers intensively, cascade model into the departments, champions with allocated time.

  5. Sustainability & updates

    , monthly labs, semi-annual refreshers, onboarding integration, regulatory updates.

From awareness workshop to lasting AI capability

Own dataRemote/on-site

Training only works when participants actually work during the session — with real tools, on real tasks, with their own data (where data protection allows) or with anonymised examples. Slide marathons with Q&A at the end change no one's way of working.

Format building blocks, freely combinable:

  • Live demo — the trainer shows a workflow in a real tool, with all its stumbling blocks
  • Guided exercise — participants follow step by step, identical task for everyone
  • Solo work in a sandbox — participants apply what they learned to their own examples, trainer walks around
  • Group task — 3–4 participants jointly develop a use-case prompt, present at the end
  • Q&A in context — questions are not collected at the end but answered directly in the module when they arise

On-site, remote, hybrid:

  • On-site — best learning outcomes, ideal for sensitive topics and larger groups with mixed prior knowledge
  • Remote (video call) — cheap, cross-location, good for awareness and refreshers; less suited for intensive hands-on days
  • Hybrid — core team on-site, remote sites dialed in; it works, but needs good tech and an additional moderator

Sandbox environment instead of live systems:

  • Own test instance of Open WebUI or the cloud tool in use — participants experiment without fear of breaking anything
  • Synthetic or anonymised data — realistic, but without personal data
  • Prepared prompts and counterexamples — participants also learn what doesn't work and why

Every workshop ends with three concrete outcomes per participant: an own prompt solution for a real task, an entry in the team's prompt library, and a self-assessment of where they see themselves after the workshop.

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RefreshersOnboarding

A one-off training doesn't change how people work in any lasting way — certainly not in a field where models, tools and the legal situation shift every few months. Lasting AI capability needs ongoing upkeep, not just a workshop date in the calendar.

What continues after the first workshop:

  • Shared prompt library — versioned, categorised, with sample input and output. Power users maintain it, everyone benefits
  • Cheat sheets & quick-reference cards — printed or digital, common use cases on a single page
  • Monthly lab sessions — 30 to 60 minutes, new tools, new use cases, open questions from everyday work
  • Semi-annual refreshers — what has changed in tools, models, the legal situation; what have we learned
  • Q&A channel — Slack, Teams or Mattermost, where questions and answers accumulate as a growing knowledge base

Onboarding new employees:

  • AI awareness module is part of standard onboarding, not optional
  • Mandatory acknowledgement of the internal AI policy with the tool approvals
  • First own prompt exercise with the department's AI champion in the first work week
  • Pointer to the prompt library and Q&A channel as ongoing resources

Update discipline on regulatory changes:

  • With the next AI Act stage (high-risk duties from August 2026), requirements grow — training content is adjusted with the AI officer
  • New tools in the stack (e.g. a second self-hosted model) get a 30-minute introduction slot
  • Incidents (e.g. a near-miss data protection issue) are reworked anonymised as a learning example in the next refresher

Success indicators here aren't participant numbers, but: is shadow AI shrinking? Is the prompt library growing? Are fewer routine questions reaching IT? Are the Art. 4 AI Act compliance duties demonstrably met?

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GDPRMultipliers

Sounds interesting?

Let's talk it through in a free intro call and see how this would work for you.

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