Description:
Get hands-on with AI tools like ChatGPT, Microsoft Copilot, Gemini, Claude, and LLaMA. Learn how to write effective prompts yourself and use these tools for tasks you perform daily for your job.

Learning objectives:

• Briefly explore basic principles of AI and language models.

• Formulate effective prompts for practical work applications.

• Practice yourself with AI tools for writing, summarizing documents and brainstorming

For whom: Anyone who wants to use AI practically to work more efficiently.

Description:
Learn how to use AI tools like Copilot in Excel, ChatGPT, Gemini, Claude, and AI integrations in Power BI, Tableau, or Qlik to analyze data faster, generate insights, and build visualizations.

Learning objectives:

• Analyze, summarize and interpret data using LLMs.

• Automatically generate formulas and graphs in Excel with Copilot.

• Let AI suggest relevant analyses and next steps.

• Use AI in Power BI, Tableau and Qlik for visual dashboards and explanatory insights.

For whom: Professionals who want to use AI to work smarter with data, without writing code.

Description:
Learn how to use AI tools like ChatGPT, Claude, Gemini, and image and video tools to accelerate marketing campaigns, create content, and better understand and serve customer segments.

Learning objectives:

• Generate marketing texts for emails, advertisements and social media.

• Using AI to create visuals and videos for campaigns.

• Develop customer personas and content strategies with prompts.

• Accelerate A/B testing and optimize headlines with AI tools.

For whom: Marketing professionals and communications specialists who want to use AI for faster and smarter campaigns.

Description:
Learn how to use AI intelligently across the entire HR process — from writing job descriptions to assessing candidates and supporting onboarding and development. Discover how you can save time, increase quality and personalize processes using AI tools.

Learning objectives:

• Generate and optimize job postings with AI.

• Develop interview questions and assessment criteria.

• Screen CVs and cover letters faster.

• Personalize and automate onboarding materials.

For whom: HR professionals, recruiters and team leaders who want to use AI to make their processes smarter and more efficient.

Description:
Gain insight into how AI is making customer contact centers smarter: from automatic summaries and conversation analysis to smart chatbots and agent assistance.

Learning objectives:

• Explore the possibilities of AI for email, chat and telephony.

• Understand how AI provides real-time support to employees.

• Insight into applications such as conversation analysis, sentiment detection and FAQ automation.

• Practical steps to implement AI safely and incrementally.

For whom: Managers and executives in customer contact who want to use AI strategically.

Description:
Use AI to create visuals, videos, and voiceovers for presentations, marketing campaigns, or internal communications—without design or video skills.

Learning objectives:

• Generating images with DALL·E and Midjourney.

• Creating videos with tools like Runway or Synthesia.

• Voice-overs and speech generation with ElevenLabs.

• Using creative AI applications for content production.

For whom: Anyone who wants to use AI creatively for communication or media.

Description:
In this knowledge session you will get an in-depth overview of Large Language Models (LLMs). You will learn how organizations use these models, what is needed and where the opportunities and limitations lie.

Learning objectives:

After this session you will know:

• What LLMs are used for today, including examples from communications, HR, policy, IT, and customer service.

• How LLMs function within AI chatbots, smart search and document analysis.

• What is needed organizationally and technically to get started with it.

For whom: Professionals who want an up-to-date and applicable overview of what LLMs can mean for their organization.

Description:
Learn how to analyze images and videos using the latest AI techniques. You will work with vision transformers for image recognition, convert videos to text with Whisper, and use LLMs such as GPT and Gemini for summaries and content insights.

Learning objectives:

• Analyzing images with models such as ViT, DINOv2 and SAM

• Apply object recognition and segmentation with PyTorch or TensorFlow

• Transcribe videos with Whisper

• Gaining insights from videos with multi-modal models like Gemini

For whom:
AI engineers and developers who want to apply AI to visual and audiovisual data.

Description:
Gain insight into the most important AI services of Azure, AWS and Google Cloud. Discover how you can build concrete AI solutions within your own cloud environment with AI services from these cloud platforms.

Learning objectives:

• Explore and compare AI services from Azure, AWS, and GCP.

• Understand how to use language, image, speech and data services.

• See the difference between out-of-the-box tools and custom solutions.

• Understand how to train AI models yourself and bring them to production in the cloud with, for example, Sagemaker or Vertex AI.

• Gain insight into costs, scalability and security.

For whom: Tech leads, cloud engineers and AI specialists who want to bring AI into production.

Description:
Learn how to protect AI systems from risks such as data breaches, prompt injection and adversarial attacks. Gain insight into best practices for safe deployment of AI and how to meet the requirements of the AI Act, among others.

Learning objectives:

• Recognizing vulnerabilities such as prompt injection and model misuse.

• Understanding adversarial attacks on image and language models.

• Applying security measures such as input filters, output assessment and logging.

• Understanding legal frameworks: AI Act, GDPR and responsible use of generative AI.

For whom: Developers, security professionals, IT professionals and policy makers who want to learn about the vulnerabilities of AI systems.

Description:
Learn how to use AI to write better copy—from emails to policy briefs—by using powerful prompts that improve your writing style, structure, and tone.

Learning objectives:

• Using AI to rewrite existing texts.

• Clear and targeted writing with ChatGPT and Copilot.

• Use prompt templates for recurring writing tasks.

• Adapting texts to different target groups.

For whom: Professionals who want to write faster and more effectively using AI.

Description:

Develop an AI agent that autonomously executes tasks using memory, tools, and multi-step planning – perfect for automating complex workflows.

Learning objectives:

• Working with frameworks like CrewAI, LangGraph and AutoGPT.

• Allow AI agents to plan, assign, and execute tasks.

• Integrate tools and APIs for real-time actions.

• Structuring memory building and long-term interaction.

For whom: Developers and tech leads who want to build or integrate AI agents into processes.

Description:
Adapt existing language models (LLMs) to your own data, tone of voice or organizational context using techniques such as fine-tuning, LoRA and embedding filtering.

Learning objectives:

• Difference between fine-tuning, prompt-tuning and context-embedding.

• Working with OpenAI fine-tuning API and Hugging Face Transformers.

• Training and evaluating domain-specific models.

• Best practices for safe, scalable model modifications.

For whom: Engineers, developers, data scientists and data specialists who want to adapt language models to their context.

Description:
Gain a practical grasp of classic machine learning techniques such as classification, regression, and clustering, including model selection and evaluation.

Learning objectives:

• Applying classical ML techniques: regression, decision trees, clustering.

• Train, validate and evaluate models (confusion matrix, F1 score).

• Feature engineering and dealing with bias or data pollution.

• Integrating ML into AI workflows and pipelines.

For whom: Data specialists, engineers and professionals with technical knowledge who want to use AI more broadly than just LLMs.

Description:
Work in teams on a realistic AI project: from devising and scoping use cases to developing a prototype and final presentation – with guidance from experts.

Learning objectives:

• Translate use cases into a technical architecture and solution.

• Develop under the guidance of experts and deliver a demo.

• Pitch a solution to a jury or internal stakeholders.

For whom: AI teams or professionals who want to deepen their knowledge by building and learning in practice.

Description:
Learn how to set up a Retrieval-Augmented Generation (RAG) system that lets AI answer questions based on your own documents and knowledge sources.

Learning objectives:

• Working with vector databases such as FAISS and Weaviate.

• Chunking strategies, metadata filtering, and hybrid search methods.

• Setting up RAG pipelines with LangChain or LlamaIndex.

• Connecting document data to a front-end or API.

For whom: AI engineers and developers who want to connect AI to internal knowledge.

Description:
Learn about the operation, architecture and evaluation of generative AI models such as ChatGPT, Gemini and LLaMA.

Learning objectives:

• Understand how LLMs work: transformers, tokens, embeddings and context.

• Methods to evaluate the output of LLMs: factuality, bias, consistency, helpfulness.

• Understanding methods to detect hallucinations

• Gain knowledge of the different types of LLMs, such as reasoning and multimodal models.

• Insights into prompt engineering vs. fine-tuning vs. RAG for specific use cases.

For whom: Engineers and technical professionals who really want to understand how Generative AI systems work.

Description:
Learn how neural networks work and apply them to practical deep learning solutions. Train your own models with frameworks like TensorFlow and PyTorch, and understand where deep learning adds value.

Learning objectives:

• Basics of neural networks, activation functions and loss functions.

• Building a feedforward network and training on structured data.

• Applying CNNs for image recognition and RNNs for time series or text.

• Using pretrained models and transfer learning for faster results.

For: Data scientists, AI engineers, and developers who want to build and understand practical deep learning solutions.

Description:
Explore the ethical aspects of AI applications. Learn how to responsibly handle bias, transparency, explainability and human control, and how to use AI in a way that is fair, inclusive and trustworthy.

Learning objectives:

• Insight into ethical risks such as bias, exclusion, automation pressure and overtrust.

• Learning to work with ethical frameworks (such as the EU AI Act, UNESCO AI Ethics, ELSA).

• Practical strategies for ethical design, testing, and implementation of AI systems.

For whom: Policy makers, AI developers, ethics advisors and organizations that want to use AI responsibly.

Description:
Learn how to professionally develop, test, deploy, and manage machine learning models using modern MLOps principles. From experiment tracking and CI/CD to monitoring and model versioning.

Learning objectives:

• Insight into the complete MLOps process: training, testing, deployment and maintenance.

• Use tools such as MLflow, DVC, Weights & Biases and Kubeflow.

• Setting up pipelines with version control, CI/CD and containerization (Docker/Kubernetes).

• Implement monitoring and retraining strategies for model maintenance.

For whom: Data scientists, ML engineers and DevOps professionals who want to bring AI into production in a robust and scalable way.

Description:
Learn how to build your own AI assistants using GPTs, no-code tools like Zapier, and smart automations — without any coding required.

Learning objectives:

• Configure GPTs with documents, tools and instructions.

• Set up smart workflows with AI + forms + output (e.g. email or PDF).

• Gain insight into secure integration via tools such as Make, Zapier or Airtable.

• Practical applications: intake flows, onboarding scripts, internal Q&A.

For whom: Professionals and teams who want to use AI to make operational processes more efficient .

Description:
Learn how to use AI to make your work as a policymaker more efficient : drafting policy proposals and texts, recognizing trends, summarizing documents and exploring scenarios using language models.

Learning objectives:

• Using AI to analyze reports, evaluations or feedback.

• Using AI as a sparring partner when writing policy documents.

• Explore trends and scenarios through data analysis and text summaries.

• Recognizing and structuring the limits of AI in decision-making.

For whom: Policy advisors, researchers and strategists who want to apply AI to perform their work more efficiently .

Description:
Get an overview of how to use AI responsibly within an organization: from usage policies to ethics, risks and roles.

Learning objectives:

• Establish internal AI agreements and frameworks.

• Define roles and responsibilities (data, evaluation, supervision).

• Understanding AI Act, GDPR and guidelines for low-risk AI.

• Integrating AI into organizational culture and change management.

For whom: MT, policy advisors, project leaders and AI managers within organizations.

Description:
Learn how to recognize AI opportunities in your organization, how to translate them into concrete use cases and how to assess them for value, feasibility and risk.

Learning objectives:

• Recognize AI applications in processes, customer contact, policy or data work.

• Structure use cases in terms of input, processing and output.

• Prioritize based on impact, risk, complexity and strategic value.

• Develop a format and assessment framework for decision-making in your organization.

For whom: Team leaders, policy advisors, innovation managers and project leaders who want to use AI where it really adds value.

Description:
In this customized training, we fully align the content with the needs, challenges and ambitions of your team or organization. Whether it concerns AI in HR, customer contact, policy, marketing, care or technology — we make the training relevant, recognizable and directly applicable.

Learning objectives (in consultation):

• Translate AI concepts to your sector, processes and working methods.

• Research and develop relevant use cases.

• Practical practice with the AI tools, software or frameworks that match your domain.

• Jointly draw up a step-by-step plan for implementation or experiment.

For whom: Teams or organizations that want to learn to apply AI practically and domain-specifically in their own context.