Training to become an AI Professional.
Eight days in which you build up AI technology, use-case prioritisation, tool selection, strategy methodology, EU AI Act, governance and ethics to the point where you can take on the driving manager role in your company. Small group, plenty of hands-on, with QCT participant certificate at the end.
Request the training→Four reasons for the QCT version.
Compliance sense included
EU AI Act, GDPR and liability questions are a module of their own, not a side note. You learn when a use case becomes a compliance case before it does.
Small group
Up to 10 participants per course. Hands-on blocks, use-case sparring and tool comparisons get the depth that gets lost in groups of 25.
Trainers with consulting practice
Led by people who run AI and compliance projects themselves. Examples and pitfalls come from real mandates, not from a textbook.
Connection to consulting
If a concrete plan grows out of the training, there is a direct path into the QCT AI compliance line. No sales pressure, just short distances.
18 modules over eight days.
Three AI families and their business profile
Rule-based, machine learning, generative AI.
- Cleanly separate rule-based systems, machine learning and generative AI
- Map strengths and limits of each AI family to use cases
- Place typical business profiles per family
- Live demo: compare three current AI models (Claude, ChatGPT, local) on the same task
Machine learning and deep learning at manager level
Training data, models, evaluation metrics, drift.
- Assess training data, models and evaluation metrics from a steering view
- Treat drift, bias and model lifecycle as a managed risk
- Discuss with data science teams on a technical level
- Own exercise: place a concrete model phenomenon against an example from your business
Generative AI, LLMs and foundation models
Tokenisation, context window, hallucinations, knowledge cutoff.
- Explain tokenisation, context window and hallucinations with confidence
- Factor in knowledge cutoff and model versioning when deciding
- Summarise how LLMs work in language that holds up at board level
- Hands-on: confront a foundation model with a task from your daily work
Prompting and critical output assessment
Prompt structures, role briefings, typical business use cases.
- Build prompt structures and role briefings for your own use cases
- Check output systematically for hallucination and bias
- Practice on your own business use cases hands-on with the model
- Workshop: own prompting playbook for three standard tasks from your area
Looking at the data situation strategically
RAG, embeddings, vector databases, maintenance effort.
- Assess RAG, embeddings and vector databases against cost and benefit
- Factor data readiness into make-vs-buy decisions
- Plan realistic maintenance effort for data pipelines
- Hands-on: check the data situation in your company against the strategy questions
Use-case identification and prioritisation
Methodology and prioritisation matrix by impact, risk, effort and data situation.
- Build a use-case inventory from your own business
- Apply a prioritisation matrix by impact, risk, effort and data
- Assess and sharpen your own use-case sketches in the workshop
- Workshop: fill and prioritise a use-case matrix on your own example
Tool landscape and vendor selection
Cloud LLM, in-house LLM, RAG pipelines, AI platforms in a structured comparison.
- Compare cloud LLMs, in-house LLMs and RAG pipelines in a structured way
- Derive selection criteria beyond feature lists
- Address vendor risk and lock-in effects up front
- Exercise: run vendor selection criteria against a concrete use case
ROI assessment and reasoning
How to ground AI initiatives towards leadership and steering committee, without slipping into marketing rhetoric.
- Apply business-case templates for four typical use-case classes
- Ground AI initiatives towards leadership and steering committees
- Translate marketing rhetoric into verifiable figures
- Workshop: set up and stress-test an ROI calculation for your own use case
AI strategy framework and roadmap methodology
From use-case inventory to a robust AI strategy.
- Derive a robust AI strategy from a use-case inventory
- Structure roadmap with maturity levels, capability build-up and vendor decisions
- Attach quarterly planning to existing strategy cycles
- Exercise: develop a 90-day roadmap sketch for your own initiative
EU AI Act, risk classes and prohibitions
Walk through Regulation (EU) 2024/1689 in a structured way: prohibited practices, high-risk systems, transparency obligations, general GPAI rules.
- Separate prohibited practices, high-risk systems and transparency duties
- Place key dates and transition periods for your own use cases
- Practice risk-class assignment on concrete examples
- Hands-on: place risk-class classification on three examples from your area
Provider versus operator: knowing your obligations
When are you a provider, when an operator, when both.
- Determine your own role as provider, operator or both
- Derive the obligation set per role from the AI Act
- Plan conformity assessment, market monitoring and incident reporting
- Workshop: provider/operator mapping for your AI stack
AIMS and ISO/IEC 42001 as a governance frame
AI Management System under ISO/IEC 42001: structure, scope, interface to ISO 9001 and ISO 27001.
- Structure AIMS scope and architecture under ISO/IEC 42001
- Cut clean interfaces to ISO 9001 and ISO 27001
- Assess the value of certification for your own organisation
- Exercise: adapt AIMS building blocks to the structures of your company
GDPR, anti-discrimination law and product liability in an AI context
Data protection, anti-discrimination and liability questions, applied to a concrete AI setup.
- Translate data protection duties into concrete AI setups
- Identify discrimination risks methodically
- Address product liability questions early in AI architectures
- Practice block: walk through AGG and GDPR anchors on a concrete case
Ethics, fairness and bias reflection
Protected attributes, fairness metrics, methodical bias checks.
- Choose protected attributes and fairness metrics for your use cases
- Anchor bias checks as a methodical routine in projects
- Apply the ethics toolkit in day-to-day work
- Reflection round: discuss and place a bias example from your own environment
Integrating AI into the business process
Typical architectures, pilot paths, make vs buy.
- Choose typical AI architectures for pilot and scaling paths
- Take make-vs-buy decisions against clear criteria
- Recognise and prevent shadow IT and isolated solutions early
- Workshop: sketch the integration of an AI component into an existing business process
Security, hallucination mitigation and red-team principles
Prompt injection, jailbreaks, data exfiltration, hallucination mitigation.
- Recognise prompt injection, jailbreaks and data exfiltration as attack vectors
- Design defense layers for your own AI applications
- Build red-team routines into the release cycle
- Hands-on: small red-team simulation on a demo application
30-day plan workshop
In small groups a concrete 30-day plan is built for your own use case.
- Turn your own use case into a concrete 30-day plan
- Sharpen priorities, risks and stakeholder alignment in sparring
- Define the first concrete action steps for the next 14 days
- Workshop block: refine the elaborated 30-day plan with trainer feedback
Use-case pitch and defense
Each participant pitches their own use case in front of plenary and trainer.
- Pitch your own use case to plenary and trainer in a structured way
- Take questions from plenary and trainer with confidence
- Sharpen the pitch and secure the eligibility for the QCT certificate
- Pitch exercise with defence round, mutual stakeholder sparring
What it costs.
What is included
- Eight full training days in a small group
- Hands-on setup with use case assessment and tool selection
- AI Professional toolkit (templates, checklists, documentation)
- Confirmation of attendance
Discounts
Early-bird discount 10% when booking more than 30 days before the date. Group discount 10% from 5 participants registered together.
In-house training?
For an on-site learning environment and a format that you can run repeatedly for your colleagues, we deliver this workshop in-house as well. Reach out for a tailored offer.
What we are often asked.
Do I need the foundations course or the intensive course beforehand?
No. The AI Professional training covers all content of the half-day foundations course and of the two-day intensive course and takes it further to manager level, with additional topics around strategy, EU AI Act, governance and ROI reasoning. If you have already attended one of the two prior courses, the first days serve as a refresher and you move earlier into the depth. If you start from scratch, the foundations are delivered to you in the first days.
Does this training cover the mandatory training under EU AI Act Article 4?
No. The AI Professional training has a broader scope and is not company-specific. If you need Art. 4 evidence for your company, combine this training with the mandatory training AI literacy under Article 4. The two fit together but are different formats.
What prior knowledge do I need?
No technical background. Experience in steering or consulting roles helps, because the modules around use-case prioritisation, ROI and compliance are discussed at a manager level. If you already use AI tools in daily work, you also benefit more in the hands-on blocks.
How much hands-on is in the eight days?
Close to half of the time. Prompting (day 2), use-case prioritisation and tool comparison (day 3), risk classification (day 5), bias review (day 6), red-team exercises (day 7) and the workshop pitch (day 8) are fully hands-on. The small group size of max. 10 participants is chosen exactly so you don't get lost in the plenary during exercises.
How are the eight days distributed?
Standard format is a block across two consecutive weeks, Monday to Thursday in each week. Fridays stay free. A spread variant runs with one training day on Fridays every two weeks across sixteen weeks when that is easier to plan inside the team. Confirmation happens at booking.
Does this make sense as a career changer into an AI Professional role?
Yes. The training is explicitly also designed for career changers from project management, IT or consulting who have taken on an AI mandate. You get the methodology, the language and the assessment sense to steer in the new role from day one.
Is this the mandatory training for AI officers?
No. If you take on an operational compliance role under AIMS / ISO 42001, you book the Training for AI officers. The manager training covers the strategic and operational driver role, not audit capability.
When is the training a better fit than 1:1 coaching?
If you want to build a methodical foundation for the role and benefit from the plenary, group training is the right way. For a concrete plan with an acute steering question 1:1 mentoring & coaching fits better. The two can also be combined, often as training followed by a short coaching engagement.
Become an AI Professional, on a real foundation.
A small group, with EU AI Act depth, governance methodology and a QCT certificate at the end. On site or remote.
Request the training→Maybe a different pillar fits your situation better.
Quality Consulting
Strategie, Methodik, Frameworks für belastbare Qualität. Audits, Konzepte, AI-Compliance.
→Quality Services
Operative Test-Manpower, Interim-Testmanagement und Vermittlung aus dem Fachnetzwerk.
→Quality Education
Workshops, Schulungen und 1:1-Coaching für Test-, Projekt- und KI-Compliance-Themen.
→CT Map
Übersicht aller drei QCT-Säulen mit Wegweiser zu deinem passenden Einstiegspunkt.
→