Building future-oriented AI skills in the company
AI skills help employees use artificial intelligence (AI) effectively and critically evaluate its results. In this blog post, you’ll learn what skills companies need for this and how they can systematically develop them.
What are AI skills? An overview
When we talk about AI skills, many people initially think of programming skills or an understanding of complex algorithms. But the reality is much more complex.
AI skills are the abilities that employees and managers need to effectively use and develop artificial intelligence in companies. They encompass a broad spectrum of skills that go far beyond purely technical knowledge. At its core, it is about understanding AI technologies, applying them in a targeted manner and critically reflecting on their impact.
The level of expertise required depends on the task: users use approved AI tools and review results. Subject matter experts design use cases and quality criteria. Technical specialists develop and integrate AI systems. Not everyone needs to program or train models themselves.
The central AI skills include:
- Data analysis: This involves the ability to collect, process and interpret information from various sources in order to gain valuable insights. This skill is important in many areas, whether it is to better understand customer behavior, optimize processes or make well-founded decisions.
- Forspecialized technical roles, knowledge of machine learning is also important. This includes an understanding of various algorithms and the ability to develop and train machine learning models. A specific use case would be the development of image recognition systems for quality control.
- Ethical competence is another important AI skill. Given the far-reaching impact of AI decisions, professionals must be able to recognize ethical implications and deal with them responsibly. This can relate to fairness in AI-supported personnel decisions, for example.
- Problem-solving skills and critical thinking are also required. Employees must be able to analyze complex challenges and develop creative solutions.
- Last but not least, communication skills play an important role. AI specialists must be able to explain complex technical concepts in an understandable way and collaborate with specialists from other areas. This is particularly relevant when implementing AI solutions in existing business processes.
Companies that specifically promote these skills create the basis for the successful integration of artificial intelligence into their organizations.
Why Companies Should Focus on Developing AI Skills
Do you remember the last major technological change at your company? Maybe it was the introduction of new software. It took a while for everyone to get used to it, and there were certainly a few challenges along the way. The use of AI is also changing the nature of tasks and requires new skills.
The key question companies must ask themselves today is therefore not just: Which AI tools should we implement? but also: Do our employees have the necessary AI skills to actively shape this transformation? The gap between existing skills and the requirements of a specific AI application can be described as the “future readiness gap.”
If you recognize this gap early on, you can take targeted measures to make your employees fit for the future of AI. The development of AI skills plays a crucial role in this.
The AI Act and the Development of AI Skills
Article 4 of the EU AI Regulation requires providers and operators of AI systems to take measures to support the development of AI literacy. In doing so, they must take into account prior knowledge, tasks, the systems used, and the context in which they are used.
The Federal Network Agency explains: Standardized training programs, external certifications, or a guaranteed level of competence for individual employees are not required for this purpose. It recommends documenting the measures taken in a transparent manner. The appropriate qualification depends on the specific needs.
A look at the AIComp competency model: the 12 AI key competencies
To support companies in the systematic development of AI skills, the NextEducation Research Group at DHBW Karlsruhe has developed the AIComp model. It is based on a study of more than 1,600 working professionals in Baden-Württemberg. The model describes 12 AI-related competency areas, which are divided into three categories:
- Work and activity: Includes skills for direct interaction with AI systems.
- Social environment: Includes collaboration and communication skills in an AI context.
- Personal development: focus on responsible handling and critical consideration of AI.
This categorization enables a holistic view of the required AI skills and supports companies in implementing targeted development measures in all relevant areas.
The study gathers subjective assessments of experiences, skills, and competency needs. The model provides guidance but is not an objective, standalone test. Which requirements are relevant to a specific job and what skills employees already possess must be assessed within the specific work context.
For companies and educational institutions, the AIComp model serves as a guide for developing training strategies. In addition to direct interaction with AI, it also takes into account collaboration, personal development, and responsible behavior. The selection of specific learning objectives remains the responsibility of the company.
Would you like to find out more about how you can develop your employees' AI skills? In our white paper, we take an in-depth look at the AIComp competency model and the topic of AI & e-learning at the Haufe Akademie.
Strategies for skills development: the importance of regular training and skills development
An effective strategy for building AI skills begins with a careful assessment of existing skills and the identification of gaps. The AIComp model provides guidance in this regard; however, it does not replace a task-based needs analysis. Building on this, you can develop targeted training measures that address both technical and cross-disciplinary competencies. It is crucial to view competency development as an ongoing process. This is because, particularly in the field of artificial intelligence, new developments and application possibilities are constantly emerging, meaning that a one-time training session or a single continuing education course will not lead to long-term competency development. Therefore, it is essential to establish regular continuing education programs and foster a culture of continuous learning.
These can take various forms, ranging from structured online courses to hands-on workshops and mentoring programs. It is important that these initiatives are flexible enough to adapt to new trends and technologies. You should also create incentives to encourage employees to take the initiative in further developing their AI skills. This combination of structured training and self-directed learning supports the ongoing development of up-to-date and relevant AI skills.
Implementing an effective AI skills development strategy requires a holistic approach. Here are some key strategies:
- Establishing a Culture of Learning
A key element of successful skills development is creating a culture of learning within the company, in which learning is established as an integral part of everyday work. This requires designated time for learning and support from management.
Not every employee the same skills at the same level. Adaptive learning systems based on AI technologies can make a valuable contribution here by analyzing learning progress and customizing content.
- Practical application
Theoretical knowledge alone is not enough to develop AI skills. Companies should create opportunities to apply and deepen AI skills in real projects.
- Promote cross-functional collaboration
AI projects often require collaboration between different departments. Encourage exchange and collaboration between technical and non-technical teams.
- Develop ethical guidelines
Establish clear guidelines for the ethical use of AI and train your employees to apply them in practice.
From Areas of Competence to Specific Learning Objectives
Hypothetical example: A company is evaluating ten complex information workflows in product development to determine their suitability for AI support. First, it addresses one case: Workshop notes are to be used to create a draft requirements list. To do this, employees must not only formulate a task but also check the draft for completeness and source references. If no implementation date is yet specified in the notes, the AI must not add a date as a finalized requirement.
The following table is an in-house editorial planning guide with five competency areas; it is not a representation of the AIComp model, nor is it a competency test.
Fictional Example: From Areas of Competence to Observable Learning Objectives
The level of specialization required depends on the role and responsibilities. A leadership position does not automatically imply the highest level of competence in every field. The overview of AI competency development in the workplace illustrates how requirements, learning needs, and appropriate learning paths can be aligned.
Skills development in companies: Why AI courses make sense
Implementing AI in a company is only the first step. True success comes only when employees have the skills needed to use AI technologies to their full potential. AI training courses can therefore be a wise investment in the company’s future viability. They not only help close the “future readiness gap,” but also enable the company to actively and successfully shape its digital transformation.
Digital self-paced courses can offer flexibility in terms of time: Employees learn at their own pace and within agreed-upon learning periods. This is particularly advantageous when dealing with a dynamic topic like AI. How up-to-date the content is depends on the specific course and how well it is maintained.
Benefits of AI courses: How AI courses can play a central role in the systematic development of AI skills
AI courses are characterized by a systematic approach that enables learners to expand their knowledge step by step. The specific content required depends on the learning objective. For general applications, factors such as limitations, appropriate inputs, and the verification of results are important. Specialized technical roles, however, require more in-depth knowledge, such as in machine learning.
Conclusion: Future-proof AI skills with the right AI courses
In addition to technical knowledge, employees need social and ethical skills to use AI effectively and responsibly. The AIComp model offers a structured approach to provide guidance. Specific job tasks and observable learning objectives highlight the skills that are actually required for a given role.
The requirements of the EU AI Regulation also call for measures tailored to specific needs. Appropriate AI courses can be one component of this; however, time for learning, expert feedback, and opportunities to apply what is learned in everyday work remain just as important.
AI courses as a solution: finding suitable offers for AI skills
At the Haufe Akademie , we offer you a wide range of solutions for developing your employees' AI skills. Our offering includes e-learning formats, classroom and online training and innovative learning concepts for digital transformation.
Learn with the e-learning flat rate from the Content Collection
Our Content Collection offers a comprehensive e-learning program with numerous AI courses. Practical exercises and application-based tasks support the transfer of knowledge into everyday work. This also requires appropriate opportunities to apply what’s learned and expert feedback. Employees can learn flexibly and according to their needs, allowing them to gain targeted training in AI topics. Our e-learning program includes hands-on courses on a wide range of topics—and new ones are added all the time!
Your employees benefit from a wide range of courses that are specifically tailored to the challenges and opportunities of artificial intelligence. These include, among others:
- ChatGPT: Learn how to use this powerful AI effectively.
- Prompt Engineering: Develop skills to create accurate and effective inputs for AI models.
- Make data-based decisions: Learn how to use data to make informed decisions.
- Optimizing products and processes with artificial intelligence: Discover how AI can improve your business processes.
Content Collection is available starting at 1.11 euros per employee per month, plus VAT, with a minimum of 250 licenses. Additional fees may apply for setup and, if applicable, consulting services. The specific offer is binding. The current terms are outlined on the product page at Content Collection.
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