Start your AI learning journey
The 5 stages of the AI learning journey
From the first steps to practical application: Find out where your company really stands in terms of AI skills and which stage offers the best starting point.
Stage 1: Getting ready to go
Gain an overview, understand goals, lay the foundation.
The aim here is to create a common understanding of AI: What can it do and where are its limits? What opportunities does it offer? The goal is to lay a solid foundation that arouses curiosity and triggers initial moments of insight.
- This stage brings the following benefits: Everyone in the company speaks the same language—discussions are clearer and misunderstandings are reduced.
- Examples: compact overview training courses, AI literacy programs (also available as a library or day-long format), basics of data analysis and visualization, e-learning modules on cloud topics and data protection.
- Good for: All employees, departments before pilot launch, management for common vocabulary
Stage 2: Getting started
Try out methods and tools, experience initial successes.
This phase is practice-oriented: AI tools are tested, use cases are implemented, and initial results become visible. The focus is on gaining experience, rapid learning effects, and measurable improvements in everyday work.
- What this stage brings: The first quick wins become visible. Tasks are completed more efficiently, routines run more smoothly, and teams immediately feel the benefits.
- Examples: Workshops on prompt techniques, application-oriented training for HR, finance, purchasing, or project management, text and presentation workflows with AI, no-code/low-code approaches for data projects, practical sessions on Microsoft 365 & Copilot, sales training on AI-supported marketing & sales.
- Good for: specialist departments & power users (HR, finance, marketing, purchasing, service, PM), all employees with a practical focus
Stage 3: Level up
Deepen competencies, refine processes, increase impact.
This is where things get serious: setting standards, ensuring data quality, increasing efficiency. Teams deepen their skills and set up structures that make AI use sustainable and efficient.
- This stage brings the following benefits: decisions are based on better data, error rates decrease, and processes become significantly more reliable.
- Examples: Seminars on data governance and data quality, training courses on forecasting and reporting with AI, advanced data modeling and visualization (Power Query, DAX, dashboards), several months of further training in analytics/BI, formats for data storytelling and decision-ready work with data.
- Good for: Departments with recurring AI workflows, PE for clarifying roles and responsibilities
Stage 4: Making an impact
Plan strategically, drive innovation, get others on board.
Here, foresight and clear guidelines are essential. Managers drive scaling forward and actively involve the organization.
- What this stage brings: AI becomes predictable—clear rules create trust, projects can be scaled, and managers provide guidance.
- Examples: AI as part of corporate strategy, AI governance & compliance (including the EU AI Act, regulation of generative AI), AI-supported leadership & change, function-specific scaling strategies (marketing, sales, public administration), programs to support change processes and multiplication within the company.
- Good for: Management & PE leadership, division managers as multipliers
Stage 5: Securing the future
Consolidate standards, anchor them sustainably, build trust.
The focus is on long-term thinking and expertise. Roles, career paths, and communities ensure that AI skills grow within the company—even when it comes to new technologies.
- What this stage brings: AI competencies grow continuously, dependencies on external partners decrease, and internal communities drive progress.
- Examples: Expert paths in AI/ML & automation, continuing education for data science & analytics (Python, ML engineer), security and audit programs, continuous review cycles and regulatory updates, learning libraries for data & AI literacy.
- Good for: Data and tech teams, centers of excellence, leadership & PE for talent paths
Stage 1: Getting ready to go
AI Literacy: skills for the safe use of artificial intelligence
(20)1 day
training
German
AI Express
(126)2 modules of 4 hours each
training
German
More creative and innovative with artificial intelligence
(51)2 days
Training
German
Successfully operationalizing artificial intelligence in the company
(115)20 hours over four weeks
Blended learning
German
Identify & economically realize AI projects
(83)approx. 20 hours over 3 weeks
Blended learning
German
AI tools for beginners
(139)1 day
Webinar
German
6 hits
Stage 2: Getting started
Prompt Engineering for ChatGPT, Microsoft Copilot and Co.
(227)1 day
training
German
AI use cases: identifying use cases, exploiting potential
(16)1 day
training
German
Successfully operationalizing artificial intelligence in the company
(115)20 hours over four weeks
Blended learning
German
Using AI technologies and automation tools in a practical way
(22)approx. 20 hours over 4 weeks
Blended learning
German
Mastering the Future I – Using AI successfully for marketing and sales (basics)
(135)1 day
training
German
AI in performance marketing
(8)1 day
Training
German
AI in personnel development
(58)2 days
training
German
Create AI-supported presentations
(61)1 day
training
German
Use ChatGPT & Co. effectively and efficiently in project management
(132)1 day
training
German
9 Hits
Stage 3: Level up
AI in consulting: from workflows to value creation
(1)1 day
training
German
Data governance
(102)1 day
training
German
Strategic data competence for decision makers
(19)2 days
training
German
Successfully managing data quality
(73)2 days
training
German
Sustainable data management: strategies and operational implementation
(15)2 x 4 hours
training
German
5 hits
Stage 4: Making an impact
AI expertise for compliance managers
2 days
training
German
Management Challenge: AI for managers
(26)2 days
Training
German
Practice-oriented AI workflows: Theory and application
(1)approx. 20 h over 4 weeks
Blended learning
German
Strategic data competence for decision makers
(19)2 days
training
German
Team leadership and artificial intelligence
(36)2 days
training
German
AI in management
(69)2 days
Training
German
6 hits
Stage 5: Securing the future
certified AI-Officer
3 days
Course
German
certified AI Expert:in Compliance
4 days
Course
German
certified AI business expert
see details
Course
German
certified KI-Expert:in Vertrieb / KI-Expert Sales
4 days
Course
German
Manager:in for AI-supported transformation
2 days
training
German
certified AI Expert:in Product Management
4 days
Blended learning
German
6 hits
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