AI training as a competitive advantage
Why companies must invest in structured AI skills now
Artificial intelligence offers companies the opportunity to make processes more efficient and to purposefully evolve existing systems. It is therefore all the more important to unlock its full potential. Yet, this is precisely where many companies face a major challenge: it is often unclear how to not only deploy AI but also use it in a strategically meaningful way. This article shows why investing in building AI competence is now critical and why structured, modular learning journeys are the key to sustainable operational capability.
Between innovation pressure and the skills gap
Artificial intelligence is no longer an unknown quantity. Even if its specific impact over the coming years cannot yet be fully foreseen, we know that it has the power to fundamentally reshape business models and decision-making processes every single day. The resulting competition puts companies under pressure. This pressure forces them not only to rethink and adapt their processes but also to train their employees to use these new technologies and continuously develop them further.
Especially when companies consciously decide to implement artificial intelligence, we as a consulting firm see a gap between future professional requirements and current skill levels. This gap poses a threat to companies and demands swift action, as it leads to uncertainty, overwhelm, and a lack of agility while the competition is already pulling ahead.
Upskilling instead of downsizing
In practice, it is evident that the importance of professional qualification and employee training in the context of new technologies is still not sufficiently prioritized in many companies. While investments are made in new systems and tools, the targeted development of the skills required to use them often lags behind. According to the findings of the IBM Institute for Business Value , however, 40% of employees will need to be reskilled by 2027 because artificial intelligence has fundamentally changed, or will change, their workflows and job profiles.
This shift causes uncertainty for many employees regarding their professional future. Yet, education is essential here, because artificial intelligence does not necessarily lead to job losses; it can also create new roles. However, to fill these new positions, both the adaptation of companies to the new circumstances and the AI skills of the employees are of great importance.
AI literacy is a must: The EU AI Act
AI has become a part of our everyday professional lives. Companies have the power to decide how they leverage these new opportunities. However, the choice of whether to train their employees is no longer entirely up to them. Starting in February 2025, the EU AI Act will require companies to train their staff and introduces a mandatory requirement to provide proof of sufficient AI literacy (Art. 4 AI Act). In practice, however, these requirements remain largely vague, leaving room for interpretation regarding the specific design of training measures.
Starting in February 2025, the EU AI Act will require companies to train their staff and introduces a mandatory requirement to provide proof of sufficient AI literacy (Art. 4 AI Act).”
AI literacy can be described as the knowledge and understanding of how to use AI, and it also includes awareness of the opportunities, risks, and potential harm that may result from its use. Beyond technical skills, legal and ethical knowledge is also essential for making informed decisions in the workplace.
AI literacy fosters innovation and corporate competitiveness by increasing employee efficiency and productivity. For example, artificial intelligence helps automate repetitive daily tasks, allowing the time saved to be used for more strategic work.
Furthermore, a conscious approach to artificial intelligence minimizes potential security risks that can arise from a lack of knowledge, such as when sensitive company information is inadvertently entered into tools or systems. Therefore, AI literacy means more than just using tools. It is about the meaningful application of AI, a new mindset, and the ability to identify and further develop AI potential.
Twelve fields of competence for the future
The complexity of AI literacy can be further explained using the AIComp model. According to this model, AI competencies can be divided into twelve fields, which are reflected in three dimensions: work & tasks, personal development, and organization & social environment. An individual's competence profile is therefore comprised of the following skills:
1. Activity and implementation competence
2. System design skills
3. Creative problem-solving skills
4. Critical digital literacy
5. Decision-making skills
6. Self-efficacy
7. Critical thinking
8. Active management capability
9. Self-determination
10. Ethical competence
11. Collaboration skills
12. Communication skills
From individual training to learning architecture
To fully unlock the potential of AI skills within your company and leverage AI strategically, you must understand that AI literacy is multidimensional. Developing such a competency profile requires systematic learning architectures. AI literacy is not built through a single training session, but through structured, progressive learning modules that address various dimensions of competence.
However, this skill development does not happen automatically. While employees can acquire knowledge about artificial intelligence on their own, progress often remains fragmented without strategic integration. Companies therefore have a responsibility to manage qualification efforts purposefully and create the necessary framework. While individual training sessions can provide a quick start, they are insufficient for long-term, sustainable skill development.
“AI literacy is not built through a single training session, but through structured, progressive learning modules that address various dimensions of competence.”
Many companies have yet to establish a formal training culture for AI. Our experience shows that AI skills are often developed only on an ad-hoc or project-specific basis rather than being systematically embedded. This makes it all the more critical to treat employee development not as a matter of individual initiative, but as a strategic corporate priority.
Modular AI learning journeys for growing AI expertise
Systematically building AI skills requires sustainable learning concepts. One approach is modularization. At its core, modularization means breaking a whole down into individual parts. In the context of professional development, modules are self-contained learning units with their own clearly defined learning objectives. These individual modules are then combined into structured learning journeys.
“To structure a learning journey optimally, a needs analysis is required at the outset.”
These learning journeys foster employee development through a mix of foundational training, workshops, and specialized courses tailored to different employee groups. This allows for both a general understanding of AI and the exploration of practical applications within their specific roles. To structure a learning journey optimally, a needs analysis is required at the outset. This analysis should identify which AI systems are currently in use, who is using them, and what existing skills those groups possess. This facilitates the development of targeted training sessions.
Building sustainable AI expertise is complex because it is dynamic, role-dependent, and subject to regulatory requirements. Isolated training sessions are ill-suited to manage and evolve with this complexity. Instead, sustainable skill development requires structured and modular learning architectures.
Are you looking to build AI expertise in your company in a structured way?
