What AI skills do your employees need—and how can these be turned into training that makes a difference in their day-to-day work? The Institute for Digital Competence ( Haufe Akademie ) supports companies with digital learning content, AI-In-house training , and the AI Leadership Program. Combine foundational knowledge with practical expertise in your field and targeted leadership training. We’ll advise you on selecting the right training modules for your target groups and specific circumstances.
AI literacy means understanding artificial intelligence, applying it to appropriate tasks, critically evaluating the results, and considering the consequences of its use.
This includes knowledge, practical skills, and a thoughtful approach to opportunities and risks. A well-formulated prompt is one building block. Equally important are professional judgment, an understanding of data, and clear lines of responsibility.
For companies, the key question is: What AI skills do people need in their respective roles? A sales representative needs different areas of expertise than a data protection officer. Both need a foundation that aligns with their tasks and the systems they use.
Why take a systematic approach to building AI expertise within a company? In the 2026 Bitkom survey, 87 percent of companies using AI reported that they were tapping into AI’s potential only to a limited extent or not at all. This indicates a need for improvement.
AI training is also part of corporate development: In the KPMG CEO Outlook 2026, 74 percent of the 125 German CEOs surveyed stated that they are expanding continuing education and retraining programs. The survey included companies with annual revenue exceeding $500 million. For your planning, this means: Align training with changing job responsibilities and a specific application goal.
Specialized technical roles require additional skills, such as those needed for model development, integration, or operations. You should plan for these areas of specialization separately. Not all employees need to be able to develop AI systems themselves.
Start with the tasks for which your company wants to use AI. Describe the required skills and compare them with the current level of expertise.
This difference—the skill gap —determines the training needs. Use discussions, surveys, and collaborative testing of typical use cases. Invite employees to explore possibilities, ask questions, and share their experiences.
Which outcome should be improved? Specify specific tasks as well as requirements regarding quality, time, and responsibility.
Who uses, develops, or oversees AI? What specific tasks and decisions are associated with each role?
What systems and features are actually available? Check access rights, authorized use, and relevant differences between versions.
What data may be used? Which decisions require human review, approval, or technical or legal clarification?
Combine self-assessments with testing specific use cases. Tool experience alone says little about how reliably someone evaluates results.
What learning times, devices, and access options are available? Also, clarify who will provide guidance on practical application, feedback, and content updates.
The role name alone does not determine a learning path. The key factors are the task, prior knowledge, the systems used, and responsibilities. The following focus areas help with classification.
This process describes the planning and further development of the training program. The phases may overlap. Rules and secure access must be clarified before practical testing begins. A pilot program for a future rollout does not replace measures that are already required for people who use AI today.
Select relevant work tasks and describe the desired benefits as well as the limitations of AI use. Clarify which systems, data, and rules apply.
Result: A shared mandate with prioritized use cases.
Leadership and stakeholders: Business units and executive management, supported by HR, IT, and relevant governance functions.
Identify learning objectives for each role and assess existing knowledge through discussions and jointly tested use cases. Prioritize knowledge gaps based on their importance to the task and the potential consequences of errors.
Result: An overview of training needs with clear learning objectives.
Leadership and participants: Human Resources Development in collaboration with subject matter experts.
Combine foundational knowledge, in-depth study, practical application, and feedback. Define learning time, approaches, and responsibilities. Align the delivery with your existing learning environment.
Result: An actionable learning plan with appropriate components.
Lead and participants: Human Resources Development, business units, and IT.
Test the planned learning path with a suitable group. Evaluate clarity, tool accessibility, and use cases. Use feedback and work results to adjust the content and workflow.
Result: A revised learning path and clarified prerequisites for the rollout.
Leadership and participants: Human Resources Development, along with a pilot group and subject matter experts.
Provide the appropriate resources to the relevant roles. Agree on specific use cases and opportunities to share experiences. Managers should set aside time for learning; champions can provide support during implementation.
Result: Professional development with guided opportunities for practical application in everyday work.
Leadership and participants: Human Resources Development and managers; additional support staff as needed.
Evaluate competence, application, and results based on the agreed-upon criteria. Adjust learning paths as tools, tasks, or rules change. New employees receive an appropriate introduction.
Result: Updated learning paths and decisions regarding further actions.
Leadership and stakeholders: Human Resources Development and subject matter experts with IT and governance functions.
To scale effectively, you need reusable building blocks, clearly assigned sections, and people responsible for updates. Review content regularly and whenever specific changes are made. This way, your offerings grow in line with demand, rather than assigning more and more content to all employees.
Choose the format based on learning objectives, the need for hands-on practice, and the circumstances. Self-directed learning may be suitable for establishing a common foundation; guided practice is helpful for complex tasks. Often, the most effective learning path results from a combination of both.
Before training, check which features are included in the available versions and licenses. It should be possible to test the tools in practice using approved tools and appropriate data. Also, clarify the devices, access, and deployment in your learning environment. Our digital learning content can generally be integrated into your existing LMS . Even without your own LMS , e-learning courses are available through the Haufe Akademie learning environment. We’ll tailor the delivery method to the course offerings and your learning environment.
Executives shape how AI is used within the company. They provide guidance, clarify responsibilities, and help decide which tasks will change. To do this, they need their own experience with AI and the ability to assess outcomes and risks. Their training should address specific leadership situations: What can the team try out? Who reviews the results? How do we deal with uncertainty?
For C-level executives, strategic priorities, resources, and company-wide responsibility come into play. A meaningful learning objective is making an informed decision about which AI initiatives to pursue and under what conditions. The appropriate training depends on your current situation.
The AI Leadership Program from the Haufe Akademie addresses AI as a leadership task. Its six areas of expertise and combinable modules support development tailored to the target audience and corporate context.
Simply completing a training program does not guarantee a reliable routine. Competence develops through trial and error, feedback, and repeated practice. To facilitate this, create conditions that are appropriate for the tasks and risks involved. Also, make it clear which decisions are still made by people.
Give employees the opportunity to experiment with AI using approved cases and data. Agree on quality standards before incorporating the results into actual processes.
In addition to the training program, be sure to set aside time for practical application and feedback. Managers should make this time available as part of the daily work routine.
Discuss results and setbacks. Subject matter experts assist with quality assurance; peer-to-peer exchanges help the team identify helpful approaches.
Explain the goals, boundaries, and responsibilities. Address questions and concerns. Acceptance comes more easily when employees can help shape the benefits and outcomes.
Before you begin, define what you want to change and how you will measure progress. Distinguish between reach, capability, implementation, and business impact. Collect only the necessary data and coordinate the analysis with the relevant departments.
Would you like to develop a structured AI learning journey from individual learning opportunities? This white paper outlines a five-step process—from the starting point to long-term implementation. It provides guidance on engaging different target groups and planning progress. Use it to gain a deeper understanding of your training planning.
These answers will help you with your planning. Be sure to consult with your departments and the relevant personnel when making your selection.
Would you like to build AI skills in your company in a targeted manner or further develop existing programs? Please describe your goals and circumstances to us. Information about target audiences, prior knowledge, tools used, and the timeframe will help us assess your needs. Together, we’ll determine which training modules are relevant to your plans.
