Germany is at a turning point in its adoption of artificial intelligence (AI), with a growing number of organizations integrating the technology into their business strategies. Recent data indicates a surge in investment by German companies, aiming to transform areas like customer interaction, marketing, and research and development. However, a critical obstacle is hindering the full realization of this potential: insufficient attention to the underlying data quality required for successful AI implementation.
Every large language model (LLM), AI-driven workflow, and intelligent automation process relies on data. According to a Bitkom study, 36 percent of German companies are dissatisfied with the quality of results delivered by AI, often due to unstructured, poorly classified, and inadequately protected data. This lack of data hygiene is proving costly, with over 90 percent of AI projects failing to deliver a return on investment, as highlighted in the “The GenAI Divide” study.
The risks extend beyond financial losses. As AI systems become more sophisticated, they are increasingly targeted by malicious actors. Practices like LLM jacking – the misuse of company resources by attackers – and data poisoning, which aims to compromise the integrity of AI models, are on the rise. According to Bitkom, over a third of German companies are hesitant to accelerate AI adoption due to fears of such attacks.
“The path forward is clear: anyone who wants to drive their own AI transformation must create the right data foundation for it,” says Tim Pfälzer, General Manager & Senior Vice President EMEA at Veeam. “This includes ensuring the necessary data hygiene that artificial intelligence needs to work successfully and provide real added value for the company. It also means protecting this data sufficiently from malicious access.”
Pfälzer suggests that Germany has the potential to become a leader in this area, but swift action is needed to establish the necessary framework. While long-term investments in AI infrastructure are crucial, companies can immediately focus on improving their own data hygiene and security, maximizing the value of current and future investments. The AI transformation is underway, and proactive engagement is key to shaping its success.