The discussion about the so-called gender gap in the use of artificial intelligence is currently gaining attention. Studies show that women use AI tools less frequently than men in their everyday work. This development is often explained by structural disadvantages, a lack of inclusion or cultural barriers. But this perspective falls short – and distracts from the real problem: a lack of technological skills in a phase of profound digital transformation.
Artificial intelligence is currently changing almost all knowledge-intensive activities. From text generation and data analysis to automated decision-making processes, it is increasing productivity and speed in many industries. Those who master these tools gain a significant advantage in their everyday work. Those who ignore or hesitate, on the other hand, risk falling behind technologically.
Technological competence instead of symbolic debates
In parts of the public debate, the gender gap is often interpreted as evidence of structural inequality. This gives rise to calls for more gender research, additional equality programmes or specific diversity measures. However, these approaches are of limited use when the fundamental challenge is a different one: a lack of technological skills.
The digital transformation requires one thing above all else – skills. Companies are desperately seeking skilled workers with knowledge of data analysis, machine learning, prompt engineering or AI-supported automation. The bottleneck lies less in the social analysis of these technologies and more in their practical application.
Against this backdrop, the question arises as to whether government funding is actually being put to good use when it primarily flows into theoretical gender studies, while at the same time there is a massive need for technological training. Investing in AI skills, computer science, data analysis and digital training would open up significantly more opportunities for both women and men in the long term.
Use determines influence
Another point is often underestimated in the debate: influence on technological systems arises primarily through their use. Large language models, AI assistance systems and automation platforms evolve through interaction with users.
If women use these technologies less frequently, they also gain less practical experience in using them. In the long term, this can lead to them being less involved in strategic technology decisions – not because of structural exclusion, but because of a lack of practical experience.
The crucial question is therefore not who talks about AI, but who works with it.
Personal initiative as the key
Technological development is currently progressing at a rapid pace. New tools are emerging every month, companies are experimenting with automated workflows, and entire occupational fields are changing. In this environment, it is less the political debate about opportunities and risks that matters and more the individual willingness to actively use new technologies.
This is where employees themselves have an important responsibility. Instead of focusing primarily on structural barriers, a stronger focus on further training, experimentation and technological curiosity could help to overcome existing differences more quickly.
Many successful examples show that women can play an important role in tech fields or AI-driven start-ups – if they consciously choose this path and build up the relevant skills.
Responsibility of companies and the education system
At the same time, companies and educational institutions have a responsibility to facilitate access to technological training. This includes low-threshold training courses, practical AI workshops and the integration of AI tools into work processes.
But here, too, the same applies: training only has an effect if it is actively used. Technological competence does not come from discussions about digital transformation, but from practical application.
The real question for the future
The gender gap in AI use is therefore less a question of social analysis than a question of technological qualification. Those who master the tools of the digital future shape innovation, organisations and markets. Those who do not use them remain spectators.
Instead of investing resources primarily in theoretical debates about technology, a stronger focus on AI training, technical degree programmes and digital continuing education could have a greater long-term impact. For women as well as for men.
The future of work will be shaped by algorithms, data and automation. The crucial question is therefore not who complains about this development, but who is prepared to actively help shape it.
