aideas.org

DE ↘

AI - anyone can do it!

5 weeks · online · January 18 – February 25, 2027 · first 2 weeks free

No programming knowledge required · German and English

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scaling across Europe

UPCOMING DATES

The next cohort starts soon

If enrolment for a cohort is closed, you can easily join the waitlist and we'll let you know when new spots open up.

Programme

Start

Language

Model

Status

AIDEAS Swiss+

18 January 2027

DE + ENG

Freemium

Enrolment open

AIDEAS Level Up

Spring 2027

DE + ENG

Freemium

Waitlist

AIDEAS Vibe Coding

Spring 2027

DE + ENG

Paid

Waitlist

Who is this programme for?

You don't need to know how to code.

You need to be open to something new.

You want to gain practical experience

... and would rather apply AI yourself than take yet another online course or webinar.

You think in concrete tasks

... and want to work on a real challenge – from your own desk or with our partner companies.

You want to decide independently

... and want to compare models and tools, rather than committing to one provider.

You want structure

... and are looking for a place where your scattered AI knowledge finally comes together – from the basics to legal aspects to AI agents.

HOW IT WORKS

Two free weeks → decision → hands-on part with a team.

ONE PROGRAMME, FIVE WEEKS

What the programme looks like?

From self-paced learning to a live demo at a partner company. Sign up for AIDEAS → Kick-off → two free weeks → decision → hands-on part with a team → demo.

WEEK 1–2

≈8 hrs/week

Self-paced learning

Decision...

continue?

From week 3:

450 CHF for the

hands-on part

WEEK 3

Hands-on part begins

Teams form and receive a case from a partner company.

WEEK 4

Teamwork and expert sessions

Working on the challenge, supported by subject-matter experts.

WEEK 5

Finale: live demo

Demoing the agentic systems you designed to the partner company.

ONE PROGRAMME, FIVE WEEKS

What the programme looks like?

From self-paced learning to a live demo at a partner company. Sign up for AIDEAS → Kick-off → two free weeks → decision → hands-on part with a team → demo.

WEEK 1–2

≈8 hrs/week

Self-paced learning

Decision... continue?

From week 3:

450 CHF for the

hands-on part

WEEK 3

Hands-on part begins

Teams form and receive a case from a partner company.

WEEK 4

Teamwork and expert sessions

Working on the challenge, supported by subject-matter experts.

WEEK 5

Finale: live demo

Demoing the agentic systems you designed to the partner company.

Partners

GETTING STARTED

Getting started — before you begin Week 1

Introductory material, provided alongside Week 1.

What you’ll learn

How the programme works, how to progress through it, where to find support; self-assessment of competences

Overview of tools: GeneratorGPT, Microsoft Copilot, ChatGPT, Google Gemini — how they differ and what they’re used for

What you’ll do

You’ll complete the skills self-assessment and plan your own path through the programme

You’ll choose your own use case and describe the agent’s specifications

Module 1·Week 1·10 hoursFree

The fundamentals of AI and the current technological landscape

You’ll understand what AI really is, where it’s heading, and where its boundaries lie — both legal and practical.

Main objective: to build a sound conceptual model of AI, gain an understanding of the direction in which the technology is developing, and know the legal and ethical principles governing the use of AI

Key topics

What is AI — a working definition, distinguishing it from ordinary automation and from marketing ‘AI’

AI versus machine learning, GenAI and LLM models

Machine learning — how models learn, where their capabilities come from and where their limitations lie

The capabilities of AI today — the real-world scale of applications in the workplace and across industries

Limitations: hallucinations, the limits of training data, the risk of over-reliance

Roadmap: chat → multimodal AI → reasoning models → agents → multi-agent systems (LLM workflow) → orchestration of agents and tools

The AI Act, GDPR, copyright — what they actually change in day-to-day work

Ethics: the social and professional consequences of using AI

Safe use of free tools: providers’ terms and conditions, company policies, what must not be included in a prompt

What you’ll learn

Recognise whether a given solution actually uses AI, or is merely labelled as such

Distinguish between machine learning, GenAI and reasoning models, and identify which type is suitable for what

Understand why agents and agent systems have emerged now, and what distinguishes them from chatbots

Assess the legal risks of AI-generated content — text, graphics and video

Identify which data must not be entered into external tools, and why

Recognise common errors and limitations of models using specific examples

What you’ll do in practice

You’ll analyse a set of ‘is this AI?’ scenarios and compare your answers with the correct ones

You will identify 3 AI applications that can be implemented in your own work

You will find and describe a model limitation using your own example

You will analyse specific AI-generated content in terms of legal risk

Module 2·Week 2·10 hoursFree

Tools, prompting and your first agent

You’ll build a working AI agent tailored to your own use case — without writing any code.

Main objective: to move from understanding AI to practical proficiency, culminating in your own working agent.

Key topics

AI toolkit for working with text — choosing the right tool for the task

Prompting: prompt structure, context, iteration, common mistakes

Differences between LLM models and the ‘model-fits-purpose’ principle

Proof of Concept versus prototype — why test an idea before implementation

Data quality: completeness, consistency, reliability and their impact on the result

Anonymisation of personal and corporate data in practice

Six steps to building an agent

The agent’s purpose and selecting your own use case

System prompt, personality and communication style

Knowledge sources

Engine and operating parameters, including temperature

Agent rules and limitations

Tools and performance testing

Tool-based tracks

GeneratorGPT — a step-by-step guided track, free access for all participants

Microsoft Copilot — a step-by-step guided track; you must arrange your own licence

ChatGPT (your own GPT) and Google Gemini (Gem) — individual tutorial videos

In parallel: industry challenges block — learning about partners’ challenges and deciding whether to join the team-based section.

What you’ll learn

How to formulate prompts that yield reproducible, useful results

How to select the right tool and model for a specific task, rather than using a one-size-fits-all approach

How to distinguish between a PoC and a prototype, and understand what a PoC is intended to demonstrate

How to assess whether you have sufficient data to build a viable solution

Design an agent: objective, persona, knowledge sources, engine, rules, tools

Understand how parameters — including temperature — affect the agent’s behaviour

What you’ll do in practice

Go through a prompt iteration: first version, diagnosis of weaknesses, revised version

You will build an agent using your chosen tool — minimum requirements: a system prompt, at least one knowledge source, and the tools needed to complete the task

You will test the agent using your own queries and document any improvements

You will choose an industry-specific challenge and sign up for the practical (team-based) part

Module 3·Week 3·Stage 1·10 hoursPaid

Start of the practical session and solution architecture

You will translate an industry challenge into specific requirements for an agent system.

Main objective: to understand the business context of the challenge and make an informed decision regarding the architecture of an AI solution for a specific business project.

Key topics

Team collaboration principles: roles, communication, work rhythm, decision-making

Analysis of the industry challenge: who is the user, what is the real problem

From problem to objective — defining the expected business outcome

Back to the architecture map, this time from an operational perspective: a single agent, an LLM workflow or orchestration

How many agents and for what — division of responsibilities within the agent team

Choosing the technology stack: GeneratorGPT or Microsoft Copilot with Copilot Studio

PoC success criteria — how will we know the solution works

What you’ll learn

Establishing collaboration rules that will stand the test of three weeks’ work under time pressure

Translate a general industry challenge into a precisely defined objective

Choose an architecture that’s appropriate for the problem, rather than the most impressive one

Divide the task amongst several agents rather than building a single agent that does everything

Formulate the criteria by which the solution will be assessed

What you’ll do in practice

You’ll draw up a team contract: roles, communication channels, deadlines, and dispute resolution procedures

You will map out the process that the AI-based solution is intended to support: user, problem, context, constraints

You will describe the agent team’s setup: how many agents, each agent’s scope, and how they communicate

You will prepare the first draft of a presentation summarising the project

Module 4·Week 4·Stage 2·10 hoursPaid

Designing and building an agent system

You’ll build a team of agents, select the engines and check whether it actually works.

Main objective: you’ll move from the initial assumptions to a working, operational version of the system, verified in the first tests

Key topics

The persona and communication style of each agent in the system

Knowledge sources: client data versus publicly available data; preparation and data quality

Writing system prompts for agents with narrow specialisations

Selecting the right model for the role — not every agent needs the most powerful model

Performance parameters, including temperature, and their impact on the consistency of results

Context transfer between agents — where the workflow breaks down

Initial tests and prompt iteration based on results

What you’ll learn

To design a specialised agent rather than a general-purpose one

To prepare and integrate knowledge sources so that the agent actually utilises them

To select models and parameters thoughtfully, with justification

Identify where the system loses context and how to remedy this

Iterate on prompts based on observation, not gut feeling

What you’ll do in practice

You’ll create a full technical specification of the system: agents, their features, knowledge sources, parameters

You will build a functioning team of agents in your chosen tool

You will compare the performance of at least two engines on the same task

You will keep an iteration log: what was changed, what the effect was

You will submit a working version of the system and receive feedback

Module 5·Week 5·Stage 3·10 hoursPaid

Testing, integration and implementation of the solution

You’ll finalise the PoC, record a demo and demonstrate what would need to happen for it to be adopted by the organisation.

Main objective: the team delivers a tested, integrated solution along with a presentation and demo; as a participant, you’ll understand what really matters for the successful implementation of an AI project

Key topics

Building test scenarios and a comprehensive list of agent tasks

Analysing test results and prioritising fixes

Integrating components into a coherent system, automating workflows

Optimising for consistency and predictability of operation

Strategic AI implementation — three pillars

Data

Quality, availability, ownership and timeliness of sources. Without these, you end up with a demo, not an implementation.

Processes

The difference between supporting humans and a process carried out entirely by agents. What changes within the organisation when a process shifts to the other side?

People

Who will build it, who will use it, and who is wary of it. It is usually the latter group that determines the success of the implementation.

Further topics

People and change — why a good solution is sometimes rejected: fear of losing one’s job, lack of trust in the results, a sense of losing control over one’s own work

How to discuss the solution with those whose work will change — before a decision on implementation is made

Preparing a Proof of Concept (PoC) presentation

Recording a demo video — how to show how it works, rather than just talking about it

Implementation framework: process owner · data · launch costs · key risks · measure of success · impact on people

Individual AI implementation plan: what I’ll do myself, and what requires a decision from my line manager

What’s next — directions for further skills development

What you’ll learn

Design test scenarios that uncover real weaknesses in the solution

Improve the system based on results, not impressions

Integrate agents so that the whole system behaves predictably

Present the AI solution to the decision-maker

Plan your own AI implementation in a specific work environment

What you’ll do in practice

You’ll develop and run a set of test scenarios

You’ll make improvements and finalise the first version of the AI system for a demonstration

You’ll prepare a PoC presentation with implementation recommendations

You will record a demo video showing the solution in action

You will present and defend the solution during the Grand Celebration (for those who wish to)

You will write your own AI implementation plan (for those who wish to)

Extended Path

Additional modules explaining selected contexts for the use of artificial intelligence. Completing these is optional and does not affect the certificate. They are available to participants in the practical section as supplementary materials. Each one marks the start of a separate development pathway.

R16 hoursOptional

Level Up — Agent quality and security (ethical hacking)

leads to the AIDEAS Level Up pathway

Your agent is working. The question is whether it works well and whether it is secure.

Objective: the participant understands why the quality and security of an agent are a separate issue, and is able to identify threats in their own solution.

Key topics

why agents can be unstable

evaluating agents — how to measure the quality of responses

guardrails and limiting the scope of operation

prompt injection and other attack vectors

advanced prompting to ensure stability

What you’ll do

conduct a security audit of the agent built in Week 2 or the team’s PoC

perform a prompt injection test on your own agent and describe the result

R26 hoursOptional

Vibe Coding — Beyond the limitations of off-the-shelf solutions

leads to the AIDEAS Vibe Coding track

You’ll see where no-code ends and what begins just beyond that boundary.

Objective: participants understand the limitations of off-the-shelf tools, are familiar with the alternative, and are able to make an informed choice of implementation path for a specific use case.

Key topics

where no-code stops being sufficient — specific thresholds

what vibe coding is and why it has changed the entry threshold

benefits of off-the-shelf solutions: time, support, predictability

benefits of bespoke solutions: control, customisation, independence from the provider

What you’ll do

choose one of your own use cases and compare two implementation paths — an off-the-shelf tool versus a vibe-coded solution

compare them according to the following criteria: time to deployment · cost · control over the solution · vendor dependency · maintenance · risk

R36 hoursOptional

AI for managers — AI in a team today is about people, not processes

leads to the AIDEAS Navigators pathway

You know how to build an agent. Now the question is, how do you guide people through it?

Objective: participants will be able to identify the real reasons for resistance to AI within a team, lead a discussion on the matter, and plan the development of AI skills amongst their team members.

Key topics

why people don’t use AI despite having access to it — fear of losing their job, lack of time, lack of trust in the results, embarrassment at not knowing how to use it

attitudes towards AI within a team: enthusiasts, observers, sceptics, outspoken opponents — and what works for each of these groups

discussing AI in a way that doesn’t sound like a warning of job cuts

psychological safety: why people hide the fact that they use AI, and why this costs the organisation more than open use

the manager as the first user — modelling behaviour rather than issuing orders

What you’ll do

map out your team — attitudes towards AI, skill levels, potential ambassadors

draft a set of guidelines for using AI to be agreed upon with the team

R46 hoursScheduled for 2027

Creatives — Creative applications of AI in graphics, video and audio

leads to the AIDEAS Creatives track

AI-generated graphics, video and audio — from the first prompt to the finished product.

Objective: participants will learn about generative tools for working with visual media and sound, and assess their usefulness in their own work.

Key topics

AI toolkit for working with graphics

AI toolkit for working with video

AI toolkit for working with audio

supplementary materials: ‘What else can AI do?’, audio materials and podcasts

What you will do

complete mini-projects in each of the three media and exchange feedback with other participants

What you walk away with?

Four concrete outcomes that go beyond the programme itself.

An AI agent you built yourselves

Built as part of a real assignment from a partner company – not a made-up exercise. A concrete use case where you tested the effectiveness and safety of using AI.

→ You've already been through the whole process once. The second time, in your own company, you start with a ready-made blueprint.

Presenting your agent to the Partners

At the end, your team will present the solution to people who are experts in the field. They'll tell you the key criteria for evaluating how effective the implementation is

→ Visibility within a Swiss company – plus a result you can present internally.

A risk assessment applied to a real case

In line with revDSG, developed during a session with our two legal experts. You take away both a completed example and a blank template. This will speed things up significantly, but it doesn't replace legal advice for the final solution.

→ You can apply this to your own use case, without having to disclose it externally.

Your certificate

Issued by AIDEAS Europe after completing the hands-on part.

→ It confirms what you worked on – not just that you took part.

CHALLENGES AND PARTNERS

Real cases from real companies.

Every cohort works on a real challenge from a partner company – not a made-up exercise.

Team 401 | 3. cohort

How can AI be used to identify underfunded fundraising campaigns for endangered species and automatically generate campaign briefs along with video scripts?

Team 386 | 3. cohort

How can AI be used to automatically compare the features of mobile banking apps and identify functional gaps compared to competitors?

Team 625 | 3. cohort

How can AI be used to support car buyers in their purchase decision by comparing the equipment and quality of different models?

Team 409 | 3. cohort

How can AI be used to spot current trends among children and turn them into concrete, brand-safe product and activation ideas for Kubuś and Mlekołaki?

Team 417 | 3. cohort

How can AI be used to develop innovative, tailored telecom offers for Gen Z based on current market trends?

Team 134 | 4. cohort

How can AI be used to automate the manual analysis of public tender documents in the renewable energy sector for 3D Spart?

VOICES OF OUR ALUMNI

From practice, for practice

What surprised me the most? Aside from the fascinating modules on the practical development of AI agents, the strong emphasis on ethics.

Mikołaj Susek

Junior Software Developer

This fantastic experience showed me just how vast the possibilities of AI are and how much more there is still to discover, even when we think we’re “advanced.”

Mariusz Bartyzel

Senior Project Manager w EON

Let the fact that, after completing the first edition, I reached out and responded to a job posting from a company looking for an AI automation specialist serve as the best advertisement.

Wojtek Kibitlewski

AI Developer

I won't lie - it took hours of work on the details and caused me some stress. But the result? Tangible skills that I can take with me - to my job, my projects, and my personal life.

Michał Tomczyk

Key Account Manager / Coordinator

The content itself was amazing, but (and I’ll say this for the nth time) the people were its strongest point. I’m not just talking about the team I was part of or the connections on Discord, but also the Generator Rozwoju team.

Bartek Chmielewski

Marketing Strategy & Operations Leader

This wasn't just any course. It was a real-world introduction to the world of AI agents, prompt engineering, and the practical application of this technology in business.

Michał Dmowski

Founder w Prowork

What surprised me the most? Aside from the fascinating modules on the practical development of AI agents, the strong emphasis on ethics.

Mikołaj Susek

Junior Software Developer

This fantastic experience showed me just how vast the possibilities of AI are and how much more there is still to discover, even when we think we’re “advanced.”

Mariusz Bartyzel

Senior Project Manager w EON

Let the fact that, after completing the first edition, I reached out and responded to a job posting from a company looking for an AI automation specialist serve as the best advertisement.

Wojtek Kibitlewski

AI Developer

I won't lie - it took hours of work on the details and caused me some stress. But the result? Tangible skills that I can take with me - to my job, my projects, and my personal life.

Michał Tomczyk

Key Account Manager / Coordinator

Bartek Chmielewski

Marketing Strategy & Operations Leader

This wasn't just any course. It was a real-world introduction to the world of AI agents, prompt engineering, and the practical application of this technology in business.

Michał Dmowski

Founder w Prowork

Did I have any worries? Of course - just like all of us. But we made it to the end with everyone still on board, enriched by new experiences.

Sara Buczyńska

Associate Brand & Customer Manager

I completed both courses; they were both interesting, but I definitely got more out of Aideas, especially the practical part.

Borys Terlecki

Account Executive CEE

Working in groups - better than I could have ever dreamed. I did a lot of projects on my own... with AI.

Michał Smoleński

Solution Designer

I convinced my father - a retired engineer - to join your second cohort, so I’m sure I’ll still be with you, even after yesterday’s celebration!

Ewa Werner

Konsultant ds. marketingu

A brilliant 8-week training program, fantastic business partners, and over 10,000 graduates make AIDEAS one of the two largest AI skills development programs in Europe 👏

Aneta Onufer

Director of PMO

The program delivered exactly what I was searching for: clarity, confidence, and a deeper understanding of how AI transforms industries.

Klaudia Kostrakiewicz

Administrative Specialist

Did I have any worries? Of course - just like all of us. But we made it to the end with everyone still on board, enriched by new experiences.

Sara Buczyńska

Associate Brand & Customer Manager

I completed both courses; they were both interesting, but I definitely got more out of Aideas, especially the practical part.

Borys Terlecki

Account Executive CEE

Working in groups - better than I could have ever dreamed. I did a lot of projects on my own... with AI.

Michał Smoleński

Solution Designer

I convinced my father - a retired engineer - to join your second cohort, so I’m sure I’ll still be with you, even after yesterday’s celebration!

Ewa Werner

Konsultant ds. marketingu

A brilliant 8-week training program, fantastic business partners, and over 10,000 graduates make AIDEAS one of the two largest AI skills development programs in Europe 👏

Aneta Onufer

Director of PMO

The program delivered exactly what I was searching for: clarity, confidence, and a deeper understanding of how AI transforms industries.

Klaudia Kostrakiewicz

Administrative Specialist

The team that runs

the programme.

The team that runs

the programme.

Core team and local experts, including two legal experts for compliance sessions.

Led by people who know

what they're talking about

Led by people who know

what they're talking about

Sandra Lugonjic

HR Campus AG

HR Strategy Consultant

Urs Egli

Suter Howald Rechtsanwälte AG, Rechtsanwalt

Attorney-at-Law

Felix Anderegg

HR Campus AG

Head of Technology & Innovation

MSc in Business Information Systems

Aniq Iselin

PostFinance AG

AI Governance Officer

Thomas Sievering

HR Campus AG

Senior AI Engineer

François Thouvenin

Swiss Financial Market Supervisory Authority, FINMA
Legal Counsel / Lawyer

Kirk Bresniker

Chief Architect of Hewlett Packard Labs, Hewlett Packard Enterprise (HPE) Fellow oraz Vice President

Dino Metaxas

Digital Director, AI & Consumer Innovation, Europe Champion Europe Group

FOR EMPLOYERS

Built for your company too!

Whether it's about funding for your team or submitting your own challenge for the next cohort – we support companies either way.

FUNDING

Want your employer to fund your participation?

Many companies cover the cost of the hands-on part for their employees. We've prepared a PDF summarising the content, benefits, and time commitment for your manager.

Download the PDF for your manager ↗

PDF für Vorgesetzte laden ↗

PARTNERS

Want your company to join AIDEAS as a partner and work on its own AI use cases?

—

We start with your challenge, not a syllabus

—

People learn on your real case, not a made-up exercise

—

A working proof of concept, not a slide-deck recommendation

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We tell you what's actually implementable

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Dozens of teams, one challenge – yours

Download the partnership deck ↗

PDF für Vorgesetzte laden ↗

Marcin Przybysz

Starszy Referent w ZUS

Podczas nauki zdobyłem szeroką wiedzę o tym, jak działa sztuczna inteligencja, jak tworzyć skuteczne prompty, wykorzystywać narzędzia generatywne w praktyce

Angelika Borowska

Nordic Finance Specialist

Kurs dał mi konkretne narzędzia, które od razu wykorzystałam w pracy.

Karolina Mazurek

Accounting Manager w BPiON Group

Po co mi to było? Dla siebie. Bo ciekawość wygrała. Dziś wiem, jak tworzyć agentów AI, jak łączyć różne narzędzia, dane i API, żeby z chaosu powstało coś użytecznego i działającego.

Live knowledge

Free AI Webinars

Join our open online sessions led by experts. Discover how AI technology is transforming business.

Upcoming events

OCTOBER 15

6:00 PM

Process automation with AI agents

How to design autonomous agents that take work off your team's plate.

OCTOBER 29

7:00 PM

Ethics and Law in the age of GenAI

What you need to know when deploying generative AI models for commercial use.