A new industry study argues that Germany won’t win the global AI race by building its own foundation models — but by fusing existing AI with decades of engineering and industrial process knowledge. The data behind that claim: over €120 billion in revenue already generated through AI-driven product innovation.
Germany does not need a domestic OpenAI to remain competitive in artificial intelligence. That is the central argument of “Der Deutschland Case,” a study published by eco – Association of the Internet Industry, produced in cooperation with IW Consult and supported by AWS. The report combines a survey of 500 companies, a separate survey of 79 AI startups, and an analysis of roughly 55 million German job postings between 2019 and 2025.
Adoption Accelerates Sharply
According to the survey, the share of German companies using AI has risen from just 1 percent in 2020 to 40 percent in 2026 — a 118 percent increase since 2024 alone. Other recent studies place the figure between 26 and 63 percent, depending on methodology, but all point in the same direction: rapid, broad-based diffusion across company sizes and sectors. Adoption is no longer concentrated in IT and finance; the industrial sector’s share of AI-related job postings rose from 2.5 percent in 2019 to 6.5 percent in 2025, more than two and a half times its earlier level.
Three Types of AI Users
The study identifies three distinct company profiles: “AI users” (46 percent), who deploy off-the-shelf tools such as chatbots or SaaS solutions; “AI specializers” (10 percent), who actively fine-tune existing models for proprietary use cases; and “dual users” (44 percent), who combine both approaches. Companies that specialize or dualize report measurably stronger outcomes — 82 percent count as innovators, versus 70 percent among AI users generally, and average employment growth is roughly a full percentage point higher.
Startups as Translators, Not Model Builders
Germany’s AI startup scene mirrors this pattern. Among the 79 startups surveyed, 83 percent build their products on existing foundation models rather than developing new ones from scratch, and 68 percent explicitly fine-tune those models using proprietary or customer data. Just over half target industrial applications. Their competitive edge, the study argues, lies not in infrastructure but in domain depth: 88 percent cite sector-specific expertise as their key differentiator, and 71 percent point to a specialized data foundation — often machine and process data unavailable to generalist competitors.
Industrial AI as the Real Prize
The report’s authors frame this as the country’s structural opportunity. Germany’s industrial sector — mechanical engineering, automotive, chemicals, electrical engineering — generates roughly 20 percent of gross value added and is precisely where AI’s productivity effects appear most concentrated. IW Consult estimates AI could add up to 1.3 percent to annual productivity growth, translating into roughly €330 billion in additional gross value added by 2034, plus another €110 billion from innovation effects. McKinsey puts Germany’s AI-driven productivity potential as high as $486 billion by 2030, with about €96 billion of that directly in industry. Already, companies report €120 billion in revenue tied to AI-enabled product innovations — more than 15 percent of all innovation-linked revenue.
What Still Needs to Happen
The study identifies three conditions for sustaining this trajectory: broader AI skills among the workforce, especially at the skilled-worker level rather than only among specialists; reliable access to high-quality, interoperable industrial data; and continued investment in scalable digital infrastructure. A fourth factor cuts across all three — regulatory predictability. Forty-three percent of surveyed companies, rising to over half among startups and innovators, cite stable legal and regulatory conditions as a major factor in investment decisions, alongside similar findings from BDI and AWS/Strand Partners research.
Still, 60 percent of German companies report not using AI at all, most commonly citing a perceived lack of relevance to their business model rather than technical or cost barriers — suggesting the diffusion curve, while steep, still has considerable room to run.
Dr. Jakob Jung is Editor-in-Chief of Security Storage and Channel Germany. He has been working in IT journalism for more than 20 years. His career includes Computer Reseller News, Heise Resale, Informationweek, Techtarget (storage and data center) and ChannelBiz. He also freelances for numerous IT publications, including Computerwoche, Channelpartner, IT-Business, Storage-Insider and ZDnet. His main topics are channel, storage, security, data center, ERP and CRM.
Contact via Mail: jakob.jung@security-storage-und-channel-germany.de