Why are talents and skills important in the AI context?

How much competence building a company needs for using AI depends heavily on its size and goals. Large companies often broadly build up AI skills and create dedicated roles or teams. Smaller companies usually do better by qualifying specifically for a single AI use case instead of investing straight away in extensive training programs. This is because an AI application can function technically flawlessly and still bring little benefit if employees cannot reliably assess its results – conversely, a finished standard solution normally does not require an in-house AI team.

Building AI skills is an ongoing process: new use cases bring new requirements, so competence development should be closely linked to concrete AI projects and reviewed regularly. AI competence is also anchored in the European AI Act : providers and operators of AI systems must take measures to develop the AI skills of their employees, based on prior knowledge, experience and specific use.

The topic of talents and skills becomes particularly relevant when a company:

• wants to introduce an initial AI application,

• wants to integrate AI into existing business processes,

• wants to develop or adapt its own AI solutions,

• is unsure whether the team’s existing knowledge is sufficient for the planned use of AI,

• has to meet legal requirements such as the AI competence obligation from the AI Act, or

• wants to mitigate the shortage of skilled workers by specifically building internal AI skills.

Not all employees need the same training. The earlier you clarify what employees are supposed to do with the respective AI system, the more precisely and economically competence building can be planned.

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Build the right AI skills for your company in 5 steps:

• Step 1 – Start from a concrete use case
  Do not start with job profiles or training offers, but with an AI use case that your company wants to implement – for example, an AI assistant that answers customer inquiries more quickly. Note which tasks are necessary for this: knowing typical questions and good answers, providing appropriate information, checking AI answers for subject-matter accuracy, maintaining the technical solution, clarifying legal requirements. Also check how many inquiries are currently processed manually in order to assess the leverage of the project, and whether applications already in use come with suitable AI functions.

• Step 2 – Define a goal and identify existing knowledge
  Formulate a concrete, measurable goal instead of a general intention – for example: "In six weeks, an employee from customer service independently manages the AI assistant and reliably checks results." Then check not by job title, but by task, who already has the required skills today – in customer service, in IT, in data protection. It often turns out that a large part of the competence is already available, just distributed across several people. Finally, note only the actual gaps; for getting started, a simple list is sufficient.

• Step 3 – Qualify only for the concrete task
  Choose a narrowly defined qualification that can be implemented in a few weeks, instead of setting up a training program for all employees. Someone who, for example, uses an AI assistant for research and draft texts should know which information they are allowed to enter, what the system is suitable for, which errors can occur, and how to check results. A general AI training course conveys basics, but is often not enough for concrete use – on the other hand, someone who only uses a finished application does not need further training in machine learning. Avoid company-wide large-scale training as an entry point; they rarely meet the actual needs of individual teams.

• Step 4 – Define responsibilities and solutions for skill gaps
  Appoint a fixed contact person who accompanies the qualification, plans the time frame, gathers feedback, and reports progress to management. This person also decides how gaps are closed: if a skill is needed regularly, they first check whether it can be developed internally with a reasonable effort. If specialized knowledge is only needed for the integration of an AI solution, time-limited external support is often more sensible. Only when knowledge is needed permanently and cannot be built up internally does recruiting become relevant.

• Step 5 – Evaluate with real results after 4 to 8 weeks
  Agree on a fixed date for a review after 4 to 8 weeks right from the start. For this, go through five typical results of the AI application together with the employees involved: Were errors detected? Were unsuitable results adopted? Is it clear when a human must intervene? Does the application actually provide relief? Record the findings in writing and derive further qualification needs in a targeted manner. If the skill development is not in reasonable proportion to the benefit, the use case itself should also be reviewed.

You can find these and many more pieces of information on the AI Compass topic "Talents and Skills" in our download portal: