AI Race: Germany Must Catch Up

When it comes to implementing AI, Germany is still lagging behind in global comparisons. This is the conclusion of NetApp’s Data Complexity Report, which found that only 47 percent of German companies believe their business data is optimized for AI use. In comparison, this figure is 74 percent in the UK and 64 percent in the USA. Germany also ranks last in a continental European comparison.

Machine Learning Offers a Glimmer of Hope

The latest Artificial Intelligence Index Report from Stanford University paints a similarly bleak picture: more than 60 percent of AI patents filed between 2010 and 2022 originated from China. The EU as a whole accounted for just 2 percent during the same period, while the USA far outpaced Europe with nearly 21 percent.

However, there is a silver lining in the field of machine learning. Germany ranks a respectable fourth place globally, with five recognized models, while the USA dominates with 61 models, followed by China with 15. In terms of investment, the USA leads the world: in 2023, approximately 67 billion euros in private capital were invested in AI technology in the USA—almost nine times more than in China. While AI investment in the United States continues to grow steadily, the EU is experiencing a decline. Since 2022, private investment in AI in Europe has dropped by 44.2 percent.

Massive Competitive Disadvantages Loom

Germany and the EU now have a shared responsibility to close the gap with international competitors. Otherwise, they risk facing massive competitive disadvantages. AI has become essential for businesses, as the Stanford study highlights. AI technology not only boosts employee productivity but also enhances the quality of their work. It is particularly effective in bridging the skills gap between lower-qualified and higher-qualified employees, helping to mitigate the skilled labor shortage.

Faster and more efficient processes enabled by AI lead to lower costs and increased profits. In a 2024 McKinsey study, 42 percent of surveyed companies reported cost reductions due to AI, while 59 percent reported profit increases. However, to achieve such success, companies must first invest—both in upskilling their employees and in developing AI technology and data infrastructure. According to the NetApp Data Complexity Report, 40 percent of executives believe their companies will need to make unprecedented investments in AI and data management by 2025.

It All Starts with an Intelligent Data Infrastructure

Preparing a company for the profitable use of AI is no trivial task. AI processes require enormous computing power, extensive infrastructure scalability, and a vast number of storage operations per second. These processes are often distributed across public cloud, private cloud, and on-premises environments. Therefore, companies first and foremost need an intelligent, flexible storage structure and seamless integration of various cloud environments and service providers.

An intelligent data infrastructure does more than provide a flexible architecture that enables data access from anywhere. It also facilitates active data management to meet critical security and governance requirements. Additionally, it adapts in real-time to optimize performance and efficiency. The right data infrastructure plays a key role in resource efficiency and cost management. According to NetApp’s report, around 80 percent of technology executives worldwide are currently prioritizing data management and infrastructure investments to gain a dominant position in AI and secure a competitive edge.

Proper Data Management Makes AI Economical

If a company optimizes its data infrastructure to maximize the efficiency of computing and storage resources, AI solutions can become both economically viable and operationally sustainable. Otherwise, excessive operating costs can erode the actual value gained from AI. Additionally, the resource-intensive nature of AI can undermine sustainability goals. Countries leading in AI adoption are feeling these challenges more acutely than those still catching up. According to the NetApp Data Complexity Report, 50 percent of companies worldwide stated that AI has had a significant or extremely high impact on their sustainability initiatives, compared to only about one-third of German companies.

Companies need clear processes and metrics to ensure efficient resource utilization becomes a success criterion for AI projects. Investments in data governance pay off significantly in the long run: better data leads to more efficient AI processes, which, in turn, create a stronger and more profitable data foundation.

The Key to a Successful AI Strategy Lies in Data

Building an intelligent data infrastructure should be the first step in effectively leveraging AI’s transformative potential. With a holistic approach, companies can unlock the full value of their data and achieve significant improvements in business performance. Begoña Jara, Head of Germany at NetApp, confirms this:

"AI leaders have unified and well-catalogued data, robust security and compliance measures for sensitive information, and a clear understanding of how data evolves. This enables them to drive innovation in the new age of AI."

Time is of the essence. If German companies fail to keep up with AI advancements, they risk falling further behind international competitors. However, those that invest in intelligent data infrastructure today and implement a well-thought-out data governance strategy will be among tomorrow’s AI leaders.