Promoting AI skills in your company

How to qualify employees in a targeted and cost-effective manner for the successful use of artificial intelligence

Artificial intelligence is already transforming work processes, business models, and competitive structures. It is therefore becoming increasingly important for companies to specifically empower employees to use AI safely, productively, and purposefully in their daily work. However, it is not enough to offer generic training or provide individual tools. What is crucial is a strategic approach that establishes foundational knowledge for everyone and develops in-depth competencies aligned with specific roles, tasks, and corporate goals.

Many companies are still in the early stages here. A lack of training opportunities, uncertainty in dealing with AI, and unclear priorities are slowing down implementation. At the same time, demands are rising due to competition, pressure to improve efficiency, and regulatory requirements such as the EU AI Act. Those who take structured action now will create measurable added value, strengthen their innovative capacity, and secure their own future viability.

Key Takeaways: AI has long been a relevant competitive factor. Successful companies do not build AI competencies using a one-size-fits-all approach, but rather in a targeted manner based on functions and needs. Practical learning formats, continuous application, and clear business goals are key to sustainable success.

Let’s now take a closer look at this topic.

Why AI skills are crucial for businesses today

AI is no longer just a topic for the future. It is already transforming processes, business models, and competitive dynamics today. Companies that do not make targeted use of artificial intelligence are losing out on time, efficiency, and innovative strength. At the same time, regulatory requirements – such as the EU AI Act – are increasing, effectively requiring fundamental skills in working with AI. [1] Those who take structured action now will secure clear competitive advantages.

Why are many companies in Europe lagging behind when it comes to AI?

Many organizations are still hesitant because they lack clear strategies and feel uncertain about how to handle AI. According to the McKinsey HR Monitor 2025, only 21 percent of employees in Europe have received formal training in generative AI, compared to 45 percent in the U.S. [2]

In addition, there is a significant AI gender gap: in many training programs on generative AI, the proportion of women is below 30 percent. This not only slows down implementation but also reinforces existing inequalities in the labor market. [3]

What risks arise for companies without targeted AI skills development?

Without clear skills development, three key risks arise for companies:

• Productivity losses because employees do not use AI efficiently
• Misuse and compliance risks due to unsafe handling
• Competitive disadvantages because other companies scale faster

Added to this is a strategic risk: If only individual teams use AI without broad competencies developing across the entire company, the actual leverage remains untapped. AI only realizes its full potential when it is deployed in a structured, secure manner and aligned with concrete goals.

The right AI skills for every role in the company

Not everyone in the company needs the same AI skills. Training based on a one-size-fits-all approach often involves a significant investment but yields few results. A two-step approach makes more sense: a common foundation for the entire workforce, followed by role-specific skills that directly contribute to company goals.

What AI skills should all employees have?

All employees should understand what generative AI can do and where its limits lie. This includes a solid basic understanding of opportunities and risks, ethical guidelines, sound data practices, and a fundamental grasp of how to formulate effective prompts.

The goal is not to turn every employee into an AI expert. Rather, what matters is that they can use AI confidently and effectively in their daily work, for example for communication, research, time management, or creating presentations. Regulatory requirements such as the EU AI Act are also part of this foundation.

Which AI skills should companies specifically build for individual roles?

Anything beyond the basics should be guided by clear business and learning objectives. Not “everyone learns AI,” but rather, for example: Marketing reduces campaign creation time by 30 percent using AI. Sales increases the closing rate by 3 percent using AI.

This makes training controllable, measurable, and cost-effective.

What AI skills do functional departments like marketing or sales need?

Functional departments primarily need application-oriented knowledge. In marketing, this can involve using AI for brainstorming, content creation, personalization, or analytics. In sales, it’s more about research, preparing for meetings, supporting proposals, or follow-up processes.

It is crucial that skills training is always linked to specific tasks, processes, and metrics.

What AI skills do IT and technical teams need?

Technical teams require significantly more in-depth skills. These include an understanding of models and system architectures, fine-tuning, security issues, governance, and the assessment of technical risks.

Since this field is evolving particularly rapidly, continuous learning paths are more important than one-off training sessions.

What AI skills do managers and C-level executives need?

Managers do not need the same level of technical detail as a development team, but they do need a solid grasp of the strategic implications of AI. They must recognize how AI is transforming business models, processes, and the competitive landscape.

Added to this are decisions regarding priorities, risks, investments, and change processes. Those who focus solely on technology here are missing the mark. Leaders must also be able to guide their teams through uncertainty and change.

Why do human skills remain relevant despite AI?

The more routine tasks AI takes over, the more important human strengths become. For years, the World Economic Forum has emphasized the growing importance of critical thinking, analytical skills, resilience, agility, and social competence. [4]

Employees must evaluate AI results, identify errors, and make informed decisions. Leaders also need empathy to take reservations seriously and provide guidance. AI competencies and human skills must therefore not be considered separately.

How companies can effectively impart AI skills in practice

Individual workshops or a one-time online course are not enough. AI skills are developed through continuous application in everyday work. Companies achieve real progress with structured learning paths that are tailored to different target groups and closely link theory with practice.

Only when employees understand how to apply what they’ve learned specifically to their job responsibilities does lasting value emerge.

The 70/20/10 learning model fosters sustainable AI skills

The 70/20/10 model provides a clear framework: 70 percent of learning occurs through on-the-job application, 20 percent through interaction with others, and 10 percent through formal training.

For AI, this means: employees must use the tools directly within their work processes. Additionally, feedback, coaching, and collaborative formats are needed. Traditional training courses lay the foundation but are only part of the whole.

Microlearning is particularly effective for AI skills

AI is evolving rapidly, and employees’ time is limited. Short, focused learning modules can be integrated directly into the daily work routine.

Instead of hours-long training sessions, employees receive exactly the content they need for their current task. This increases application in daily work and reduces burnout.

Hybrid and social learning formats play an important role

Purely online or in-person formats usually fall short. Digital formats enable flexible learning, while in-person formats foster exchange and sustainable behavioral changes.

Learning in groups is particularly effective: Interaction with colleagues, shared use cases, and mutual feedback ensure that knowledge is put into practice more quickly.

Sandboxes and simulations provide the necessary safety net

Employees must be able to experiment with AI without taking risks. Protected environments make exactly that possible.

Here, they can work with tools, test prompts, and run through scenarios without jeopardizing real data or processes. This builds confidence and accelerates skill development.

How can AI itself be used as a learning coach?

AI can provide personalized support for learning processes. Adaptive systems tailor content to each individual’s prior knowledge. Chatbots enable realistic practice scenarios, such as sales pitches or leadership situations.

Employees receive immediate feedback and can improve their skills in a targeted manner. In this way, AI itself becomes an important component of skill development.

How companies can build AI capabilities at scale

Building AI capabilities in a sustainable way must be both effective and affordable. This is a particular challenge in larger organizations. Different roles, levels of knowledge, and work realities often make standard solutions inefficient.

Therefore, an approach that enables breadth without becoming arbitrary is crucial.

Targeted training for diverse workforces

In larger companies, not everyone learns under the same conditions. White-collar and blue-collar departments, executives, specialist departments, and IT teams have different tasks, prior knowledge, and learning needs.

A uniform format rarely does justice to this diversity. Successful training therefore takes into account the respective target groups and their specific requirements.

Individual training sessions or tools are not enough to build AI competencies

A workshop or access to a tool alone does not create AI competence. Impact only arises when content, application, and knowledge transfer work together effectively.

Without this connection, AI training often remains a cost center with no tangible effect on daily work.

What role do learning experts and learning platforms play?

Anyone who wants to build AI competencies broadly, specifically, and cost-effectively needs a well-thought-out learning architecture. Learning experts – such as those at chemmedia AG – help cluster target groups effectively, develop suitable learning paths, and consider the transfer to practice from the very beginning.

Learning platforms also ensure that content is rolled out in a scalable manner, delivered in a personalized way, and that progress is measurable. [5] This allows AI competencies to be developed efficiently across the entire company.

What are your next steps for building AI capabilities?

If you want to build AI capabilities effectively within your organisation, a structured and goal-oriented approach is recommended. The starting point should be clear business objectives: define the specific outcomes you wish to achieve through the use of AI, such as greater efficiency, improved service quality or faster processes.

In the next step, it is worth categorising your employees by role, area of responsibility and level of expertise. Not every target group requires the same content or learning formats. Whilst a common foundation of knowledge is useful for everyone, individual departments require more in-depth skills tailored to their specific requirements.

It is equally important to identify practical use cases. Employees learn AI particularly effectively when they can apply what they have learnt directly to real-world tasks. That is why theoretical content should always be linked to concrete application in day-to-day work.

For skills development to have a lasting impact, the new knowledge must be utilised quickly and applied regularly. At the same time, progress, usage and the effects achieved should be made measurable. Only in this way will it become clear which measures are working and where further refinement is needed.

If you consistently follow this approach, you will not only generate new knowledge but also build genuine AI competence within your organisation.

Notes (partly in German):

Assess how such an approach can be implemented in your organisation and consult with experienced learning experts. The consultants at chemmedia AG are happy to discuss this with you. Alternatively, here you can find further information about chemmedia AG.

[1] EU AI Act
[2] McKinsey HR Monitor 2025
[3] Coursera Global Skills Report 2025
[4] World Economic Forum: Future of Jobs Report 2025
[5] The right learning platform for your company

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On the t2informatik Blog, you can find an interview with Nadine Pedro: