AI TRAINING FOR TRAINERS, CONSULTANTS AND AI LEADS
Train the KI Trainer 2027
The AI Capability Master Program for AI Expertise, Enablement and Transformation
Certification: Certified Professional in AI Expertise, Enablement & Transformation
Duration: 16 teaching weeks | Start: calendar week 44, 2026 | Winter break: calendar weeks 50, 2026 to 1, 2027 | Teaching resumes: calendar week 2, 2027 | Completion: calendar week 11, 2027
AI transformation requires people who can turn rapidly evolving technology into better ways of working, sound decisions and effective organisational change.
This programme develops that capability for professionals working in training, consulting, enablement, leadership and transformation.
The case for the programme
Many organisations now have AI strategies, roadmaps, use-case lists and access to tools. The more difficult task is implementation: improving work in ways that create measurable value, distinguishing lasting capabilities from short-lived trends, deciding what can be delegated and identifying where human judgement remains essential.
Train the KI Trainer 2027 addresses that task by combining technical understanding with professional AI practice, human judgement, workflow redesign and organisational enablement. Participants learn to make informed decisions about how work should change.
Programme at a glance
Course Language: German
Format
16 teaching weeks across two blocks: six weeks of technical foundations followed by ten weeks of application, integration, enablement and transformation; 48 live hours and 64 hours of independent practice and study
Cohort
Minimum 10; maximum 35 participants
Core cadence
Weekly 90-minute live sessions on the topic of the week
Applied learning
AI Co-Work Packs: 2 hours of practice plus 2 hours of self-study per week
Practice support
Additional 2-hour AI Practice Clinic every 2 weeks
Deep dives
Technology Deep Dive with Mario (4 hours); Legal Deep Dive with Julian (4 hours)
Community
Skool community
Start
week 44/2026: 27 Oct 2026
Winter break
week 50/2026 to week 1/2027
Restart
Calendar week 2, 2027; first live session 14 Jan 2027
Completion
Calendar week 11, 2027; final live session 16 Mar; programme completion 18 Mar (provisional)
Required licence
Claude Cowork or ChatGPT Work; a standard paid subscription is sufficient
Five questions guide the programme
What do I need to understand about AI to make sound decisions?
What does professional work with generative and agentic AI involve?
Where is human judgement needed?
How does AI change workflows, roles and decisions?
What do people need to learn, and what must the organisation change?
CRISP-DM provides the programme’s central framework. It begins with the business problem and then examines the technology, data, workflow and capability changes needed to address it.
Who the programme is for
The programme is designed for professionals who explain, teach or shape AI transformation, whether they work with clients or within their own organisation.
Trainers, consultants and facilitators
AI champions and AI leads
Internal advisers and L&D professionals
Specialists, leaders and transformation professionals
The role matters more than the job title. Participants are expected to help others address AI questions that require more than a demonstration of a tool.
The cohort is designed to bring together varied professional perspectives. In previous cohorts, women have represented approximately 60% of participants.
Why the programme was redesigned
Following five cohorts of the original Train the KI Trainer programme, the curriculum has been redesigned for professionals and organisations that already have some experience with AI. Foundational knowledge remains important, but participants also need to apply it to work and organisational decisions.
Generative AI is changing knowledge work. Agentic systems extend this by planning, using tools and carrying out multistep tasks. The relevant management question is therefore how to design the work, roles and controls around these capabilities.
The revised programme develops three connected capabilities: AI expertise, enablement and transformation.
Design principles
Technology grounded and tool independent: understand the underlying concepts as well as their current applications.
Live teaching is prioritised, with a manageable session schedule that leaves room for discussion.
Barbara Lampl and her senior team lead the programme and bring practical experience to the teaching.
Participants work directly with AI; no coding is required. The emphasis is on business application and judgement.
The curriculum limits time spent on short-lived tools or on building agents without a clear business purpose.
The programme retains the Nerd Core, CRISP-DM, links between technology, business and psychology, participant cases, discussion, community and applied work.
Core principle: redesign the work
The starting point is the work: the problem to solve, the decisions to improve, the tasks that can be delegated, the points where human judgement is needed and the organisational conditions required for a new way of working.
Professional AI adoption extends beyond individual productivity. Organisations need to assess technologies in context, integrate them into workflows and distinguish training needs from process, technology, leadership or governance issues.
Adding agents to an ineffective process will not improve it. The number of agents is not a useful measure of transformation; the starting point is a clear problem and a better design for the work.
Training as part of transformation
The programme works at individual and organisational levels. People need understanding, practice, new methods and the ability to exercise judgement. Organisations may need to change processes, roles, decision rights and operating conditions.
Training 5,000 people to use Copilot does not, on its own, show that work has improved. Capability building creates value when people can apply what they learn in workflows that their organisation supports.
Participants learn to identify whether a challenge concerns learning, technology, workflow, the organisation or a combination of these. They assess when training is useful and when other measures, such as champions, peer learning, process redesign, leadership action or governance, are needed.
How the programme works
1. Live sessions
Weekly 90-minute live sessions provide the technical and conceptual foundation. They explain key concepts and frameworks, allow time for questions and make complex material accessible. Recordings are available.
Live sessions and practice clinics with Barbara: 40 hours in total
2. AI Practice Clinics
Every two weeks, a two-hour AI Practice Clinic addresses participants’ actual work, including organisational situations, client projects, training plans, workflows, unsuccessful experiments and current questions. The clinics connect the weekly curriculum to implementation challenges.
3. AI Co-Work Packs
AI Co-Work Packs help participants deepen and apply the live content. Each pack combines technical material, documents and cases with practical tasks, research, comparison, critical review, verification and transfer to real work.
Claude Cowork and ChatGPT Work are the primary environments for this work. Participants may use other platforms, but support for third-party tools is outside the scope of the programme.
AI Co-Work Packs: at least two hours of guided practice and two hours of independent study each week.
Allow at least four hours per week for independent work, in addition to live sessions and scheduled clinics. Team projects and further practice may require more time.
4. Expert Deep Dives
Technology Deep Dive with Mario (four hours). Mario brings a CTO perspective to enterprise AI architecture, models and platforms, APIs, data and retrieval, security, integration, build-versus-buy decisions and agentic systems. Topics may be adjusted to reflect developments. The aim is to help participants understand technical choices and recognise when specialist CTO input is needed.
Legal Deep Dive with Julian (four hours). Julian examines the EU AI Act, data protection, copyright, liability, governance and responsibilities in the context of practical AI decisions.
Guest contributors from previous cohorts or the wider team may also take part.
5. Community and individual transfer
An active learning community and ongoing exchange through Skool
Participants’ own use cases and professional expertise integrated into the programme
Direct application to each participant’s area of work
Practical exercises tied to business needs
An individual project developed with support over the programme
Teaching methods and strategic approaches informed by 20 years of experience
Faculty and expertise
Barbara Lampl (AI Babsi)
20 years of experience in data and AI strategy
Behavioural mathematician
11 years of teaching experience
Focus on making complex concepts understandable
Barbara combines technical substance with practical application. Her teaching draws on 11 years of experience and the development of workshop formats.
Learn more about Mario, Julian and the wider team.
What participants will be able to do
The aim is to develop sound judgement across technical, legal, organisational and process questions, while recognising when specialist expertise is required.
Assess AI developments using a sound technical foundation and distinguish relevant advances from hype
Apply CRISP-DM to current AI questions
Develop and teach professional ways of working with AI
Identify where human judgement is required
Assess agentic AI in the context of work
Identify when a workflow needs to change
Design and deliver training independently
Design enablement beyond individual training sessions
Recognise when training is not enough
Contribute to AI transformation across people, work, IT and the organisation
Certification: Certified Professional in AI Expertise, Enablement & Transformation
16-week curriculum overview
1
How AI Actually Works
Models, learning, prediction and the mental foundations of what AI systems actually do
2
Data, Statistics & Uncertainty
Data quality, statistics, probability, measurement error, benchmarks and uncertainty
3
Machine Learning & CRISP-DM
Training, testing, generalisation, overfitting, with CRISP-DM as the working logic
4
Deep Learning, Scaling & Emerging Capabilities
Deep learning, multimodality, scaling and which future developments are technically plausible
5
Generative AI & Cognitive Work
LLMs, tokens, embeddings, context and inference, with a focus on the consequences for cognitive work
6
Bias, Causality & Critical AI Judgement
Bias, correlation, causality, evaluation and the entry into human judgement
7
Integration I: Implications for work
From technical understanding to implications for work, training and transformation
8
Working with AI
Context design, task decomposition, research, verification, tool use and delegation
9
Human Judgement & Evaluation
Define which outputs and decisions require human review and how they should be evaluated
10
Agentic AI & Agentic Work
What changes when AI plans, uses tools and works in multiple steps
11
Integration II: Redesign the Work
Which workflow steps remain, disappear or can be delegated
13
Designing Enablement for New Work
What form of enablement the new work actually requires
14
From Learning to Organisational Change
Assess whether processes, responsibilities and governance support the new work
15
Integration III: People, work and organisation
People, work and organisation considered together
16
Integration IV: AI Transformation in Practice
Complex real-world cases and the selection of the right intervention
Programme content in detail
The 16 teaching weeks move from technical foundations to applied AI work, workflow redesign and organisational transformation.
Part 1: AI foundations and foresight
Week 1: How AI Actually Works
The programme begins with the mental models used throughout: what a model is, how machine learning works, what is predicted and what a system learns. It also examines why some outputs can resemble reasoning. These foundations help participants assess new systems on their merits.
Week 2: Data, Statistics & Uncertainty
This week covers data quality, statistics, probability, measurement error, benchmarks and the interpretation of results. Participants learn to assess AI outputs critically, particularly when confident language masks weak evidence.
Week 3: Machine Learning & CRISP-DM
Participants examine training and testing, generalisation, overfitting, features and model logic. CRISP-DM becomes a working discipline for the programme, with business understanding preceding modelling.
Week 4: Deep Learning, Scaling & Emerging Capabilities
Week 4 covers neural networks, deep learning, scaling, multimodality and emerging capabilities. Participants assess which advances follow from scale or training methods, where limitations remain and which claims about reasoning, AGI or autonomous systems have technical support.
Week 5: Generative AI & Cognitive Work
Large language models, tokens, embeddings, training, inference, context, hallucinations and multimodality provide the technical foundation. The discussion then turns to knowledge work: research, writing, analysis, learning, communication, problem solving and preparation for decisions.
Week 6: Bias, Causality & Critical AI Judgement
The focus is on correlation, causation, bias, evaluation and common errors in interpretation. Larger models do not remove bias, and a persuasive answer is not evidence. Participants practise identifying where human judgement is needed.
Week 7: Integration I: Implications for work
Week 7 begins the ten-week transition from technical foundations to professional application. The six-week Nerd Core is connected to generative and agentic AI, emerging developments, work, training and transformation.
Part 2: AI work and human judgement
Week 8: Working with AI
Prompting is one part of a professional workflow. This week covers context design, task decomposition, research, verification, tool use and delegation, with a focus on designing reliable ways of working.
Week 9: Human Judgement & Evaluation
Human review is effective only when responsibilities are defined. Participants consider which outputs need review, what sources are required, which errors are tolerable, which decisions can be reversed and where additional experience or context is needed. Judgement is built into the workflow.
Part 3: Agentic work and workflow redesign
Week 10: Agentic AI & Agentic Work
Systems that plan, use tools and complete multistep tasks can change how work is organised. Participants assess these capabilities and their limits, focusing on outcomes, controls and the quality of the workflow rather than the number of agents involved.
Week 11: Integration II: Redesign the Work
Participants return to CRISP-DM and the business problem. They examine which steps are necessary, which can be removed or delegated, where human judgement is required and how roles may change. The result is a redesign of the workflow around the desired outcome.
Part 4: Enablement and organisational transformation
Week 12: People, Roles & Capability
Participants identify who needs to understand, use, assess and decide what as roles change. Capability requirements follow from the work, rather than from a standard course catalogue.
Week 13: Designing Enablement for New Work
The starting point is the desired outcome: what should people do differently? Participants then select the appropriate combination of training, collaborative work, practice, champions, guidance and support.
Week 14: From Learning to Organisational Change
Participants assess whether processes, responsibilities, access, governance and leadership support allow new skills to be used. Training can contribute to transformation when the required operating conditions are in place.
Week 15: Integration III: People, work and organisation
A real case is examined across people, work and organisational conditions. Participants identify what people need to learn, how the work should change and what the organisation must enable. The appropriate first intervention may be something other than training.
Week 16: Integration IV: AI Transformation in Practice
The final week applies the full framework to complex cases. Participants diagnose the situation and choose a suitable intervention.
Typical statements used in the final integration include:
“We need an AI Academy.”
“Our people don’t use Copilot enough.”
“We want to roll out Agentic AI.”
“We want 37 agents.”
“We need an AI strategy.”
The group asks why each proposal has been made, separates the technology, work, learning, judgement and organisational dimensions, and determines the next steps.
Detailed course schedule
Block 1 (2026)
Session 1 (early): Tuesday, 27 Oct 2026 | 08:30 to 10:00
Week 2 (CW45)
2 to 8 Nov 2026; Note: 3 & 4 Nov blocked
Session 2 (late, exception day): Thursday, 5 Nov 2026 | 16:30 to 18:00
AI Practice Clinic 1 (exception day): Thursday, 5 Nov 2026 | 12:00 to 14:00
Week 3 (CW46)
9 to 15 Nov 2026; Note: 10 Nov blocked
Session 3 (late): Thursday, 12 Nov 2026 | 16:30 to 18:00
Week 4 (CW47)
16 to 22 Nov 2026
Session 4 (early): Tuesday, 17 Nov 2026 | 08:30 to 10:00
AI Practice Clinic 2: Wednesday, 18 Nov 2026 | 12:00 to 14:00
Week 5 (CW48)
23 to 29 Nov 2026
Session 5 (late): Thursday, 26 Nov 2026 | 16:30 to 18:00
Week 6 (CW49)
30 Nov to 6 Dec 2026
Session 6 (early): Tuesday, 1 Dec 2026 | 08:30 to 10:00
AI Practice Clinic 3: Wednesday, 2 Dec 2026 | 12:00 to 14:00
Winter break
CW50 2026 to CW1 2027 (7 Dec 2026 to 10 Jan 2027); no classes
Block 2 (2027)
Session 7 (late): Thursday, 14 Jan 2027 | 16:30 to 18:00
Week 8 (CW3)
18 to 24 Jan 2027
Session 8 (early): Tuesday, 19 Jan 2027 | 08:30 to 10:00
AI Practice Clinic 4: Wednesday, 20 Jan 2027 | 12:00 to 14:00
Week 9 (CW4)
25 to 31 Jan 2027
Session 9 (late): Thursday, 28 Jan 2027 | 16:30 to 18:00
Week 10 (CW5)
1 to 7 Feb 2027; Note: 4 & 5 Feb blocked
Session 10 (early, exception day): Tuesday, 2 Feb 2027 | 08:30 to 10:00
AI Practice Clinic 5: Wednesday, 3 Feb 2027 | 12:00 to 14:00
Week 11 (CW6)
8 to 14 Feb 2027; Note: 8 to 10 Feb blocked
Session 11 (late): Thursday, 11 Feb 2027 | 16:30 to 18:00
Week 12 (CW7)
15 to 21 Feb 2027
Session 12 (early): Tuesday, 16 Feb 2027 | 08:30 to 10:00
AI Practice Clinic 6: Wednesday, 17 Feb 2027 | 12:00 to 14:00
Week 13 (CW8)
22 to 28 Feb 2027
Session 13 (late): Thursday, 25 Feb 2027 | 16:30 to 18:00
Week 14 (CW9)
1 to 7 Mar 2027
Session 14 (early): Tuesday, 2 Mar 2027 | 08:30 to 10:00
AI Practice Clinic 7: Wednesday, 3 Mar 2027 | 12:00 to 14:00
Week 15 (CW10)
8 to 14 Mar 2027
Session 15 (late): Thursday, 11 Mar 2027 | 16:30 to 18:00
Week 16 (CW11)
15 to 21 Mar 2027
Session 16 (early): Tuesday, 16 Mar 2027 | 08:30 to 10:00
AI Practice Clinic 8: Wednesday, 17 Mar 2027 | 12:00 to 14:00
Schedule notes
Details may be adjusted in consultation with the group after the programme begins.
All times are in Berlin local time.
AI Practice Clinics take place every two weeks.
The schedule takes German public holidays and bridge days into account.
Weekly live sessions last 90 minutes.
AI Practice Clinics last two hours.
Investment and registration
Early Bird
€4,900 (net), until 30 Sep 2026
Regular
€5,200 (net), until 20 Oct 2026
Late Bird
€5,400 (net), from 21 Oct 2026
Payment by instalments is available on request.
Contacts
Programme team: ki-trainer@barbara-lampl.de | registration form
Quote requests: Nic Gremse-Hürten, nh@barbara-lampl.de
Barbara / consultation: bl@barbara-lampl.de | Barbara’s calendar
Book a free, no-obligation consultation to discuss your specific questions and goals.
Why this matters
AI transformation depends on people who can assess technology, improve work and build lasting organisational capability. Train the KI Trainer 2027 develops the skills to make that contribution.
For more details, contact bl@barbara-lampl.de or book a conversation directly via Barbara’s calendar.
Register for the Course