AI training in German companies lags as 56% adopt generative tools
56% of German companies use generative AI, but only 27% report employee training; survey calls for targeted investment in AI training in German companies.
More than half of companies in Germany now employ generative artificial intelligence in daily work, yet a significant training shortfall threatens safe and effective use of these tools. The finding comes from a Forsa survey commissioned by the TÜV Association showing 56 percent of firms have introduced tools such as ChatGPT, Gemini, Claude or Copilot, while just 27 percent say employees have received related training. Observers warn that AI training in German companies must accelerate if businesses are to harness benefits without elevating operational or security risks.
Adoption outpaces workforce qualification
The survey paints a clear gap between technology use and staff readiness across sectors. While generative AI has entered routine workflows, many companies report that employee competencies have not kept pace with deployment. Half of respondents describe the need for upskilling on AI as high or very high, a sign that adoption is exposing capability shortfalls within organizations.
Joachim Bühler, managing director of the TÜV Association, told researchers that companies are using AI productively but often without sufficient investment in employee qualification. He urged firms to pursue targeted training programs so that usage remains both effective and secure, particularly where automation touches regulated processes or customer data.
Big firms train more, smaller companies fall behind
Company size correlates strongly with the availability of AI training, the survey shows. Almost half of large firms with more than 250 employees reported that they had already provided training on AI tools. By contrast, only 32 percent of mid-sized companies (50–249 employees) and 21 percent of smaller firms (20–49 employees) said they had trained staff.
Experts say the disparity reflects both resource constraints and strategic priorities. Large organizations typically have dedicated budgets, internal learning-and-development teams and stronger incentives to manage compliance risks, while smaller firms often lack capacity for structured programs and rely instead on ad hoc learning or external consultants.
Companies value training but few have formal strategies
Despite the uneven rollout of training, most firms acknowledge its importance. The survey found 87 percent of respondents consider employee development either important or very important, and three quarters said training opportunities are available to all staff. Nevertheless, only 29 percent of companies reported having a written, formalized training strategy that addresses AI competencies.
The gap between recognition and planning raises concerns about consistency and quality. Without documented strategies, training efforts risk being uneven across departments and job levels, leaving critical functions exposed to misuse or misunderstanding of AI outputs.
Industry implications and operational risks
Analysts caution that insufficient AI training can produce tangible operational problems, from flawed decision-making assisted by generative models to mishandling of sensitive information. Misapplied AI in customer service, compliance reporting or product development could lead to reputational damage, regulatory scrutiny or financial loss.
Companies that integrate AI into core processes must therefore pair deployment with governance measures: defined use cases, data handling rules and role-based training. The TÜV Association’s findings underline the urgency of combining technological rollout with formal upskilling to maintain controls as models evolve.
Survey scope and methodology
The findings derive from a representative Forsa survey of 500 German companies with at least 20 employees. Fieldwork took place between January 26 and March 11 and targeted individuals responsible for training as well as CEOs and board members. The sample and timing provide a snapshot of practices in early 2026, capturing an environment where AI adoption accelerated rapidly across sectors.
By focusing on decision-makers and learning officers, the poll aimed to measure both adoption and organizational responses, including the availability of training, the existence of written strategies and perceived urgency for upskilling among staff.
Calls for targeted investment in workforce skills
Industry voices and trade associations are urging firms to invest systematically in workforce skills rather than rely on informal learning. The TÜV Association recommends that companies design targeted qualification pathways tied to specific AI applications and risk profiles, with priority given to functions handling regulated data or safety-critical processes.
Policymakers and employer groups may also play a role by subsidizing training for small and medium-sized enterprises, which lag most on provision. Stakeholders argue that accessible, sector-tailored programs could reduce disparities and make AI deployment safer and more productive across the economy.
The Forsa data underline a pivotal moment: while generative AI is increasingly part of everyday work, the human capital needed to steer and control these systems remains unevenly developed, posing a strategic challenge especially for Germany’s medium-sized companies.
As firms weigh the gains from faster workflows and new product capabilities, the survey suggests the next wave of investment will need to focus less on procurement and more on people—building the skills and governance that turn AI tools into reliable, value-creating assets.
