(de-news.net) – German industry leadership and empirical findings converge on artificial intelligence (AI) as both a potential remedy for deindustrialization and a driver of structural economic change. While the Federation of German Industries (BDI) emphasizes AI’s role in sustaining industrial value creation, mitigating job displacement, and addressing geopolitical dependencies, Ifo Institute data show rapidly rising AI adoption among self-employed and microenterprises alongside cautious optimism in business sentiment. At the same time, firms increasingly perceive AI-enabled labor substitution as feasible in specific roles, though most still regard human expertise as difficult to replace.
Peter Leibinger, President of the BDI, framed artificial intelligence as a potentially decisive lever for counteracting Germany’s ongoing and gradual process of deindustrialization, arguing in an interview that so-called “industrial AI” could develop into a central new growth narrative for the national economy. In his assessment, Germany’s comparative strength—its capacity to produce high-end, technologically sophisticated goods embedded within tightly interwoven structures of international suppliers, global markets, and long-standing customer relationships—remains largely unmatched by other national economies, and he suggested that this structural advantage could be reactivated and further amplified in the era of artificial intelligence. From this perspective, the integration of AI into industrial production processes, particularly within factory environments, would function as a mechanism to retain a larger share of value creation within the domestic economy, a development he characterized as increasingly essential. At the same time, he pointed to what he described as an ongoing structural decline, noting that industrial output has fallen by nearly 14 percent since 2018, and warning that, in his view, there is currently no discernible indication that the trajectory of deindustrialization is slowing.
In light of these developments, Leibinger expressed conditional openness to policy instruments such as a potential robot tax or AI-specific levy in scenarios where automation leads to significant job displacement in Germany. He argued that the country’s contribution-based welfare state is fundamentally dependent on the continued existence of well-compensated industrial employment, and therefore suggested that a taxation mechanism targeting AI- or robot-driven labor substitution would represent a logically coherent policy response under such conditions. However, he simultaneously emphasized that the introduction of such a measure in isolation—if pursued unilaterally by Germany—could place domestic firms at a competitive disadvantage within the global economic system. Despite these reservations, he indicated that if labor market structures continue to shift substantially under the influence of automation, Germany may ultimately have to reassess and redesign the financing mechanisms underpinning its extensive system of social insurance benefits.
Beyond the domestic economic implications, Leibinger also highlighted the possibility that artificial intelligence could increasingly function as an instrument within broader geopolitical competition. Referring to restrictions imposed by the United States on the export of advanced AI systems developed by companies such as Anthropic, he interpreted such regulatory actions as an early indication that artificial intelligence is becoming a domain of high strategic significance. In parallel, he underscored the structural dependence on large-scale language models developed outside Europe, particularly in the United States and China, describing this reliance as increasingly problematic from a strategic autonomy perspective. At the same time, he anticipated that AI would evolve into a progressively more significant cost component for industrial users. While not expected to become a luxury good, he suggested that its pricing dynamics would likely resemble those observed in other digital infrastructure services, such as office software or cloud computing, where sustained technological dependence has historically been associated with gradually increasing expenditure over time.
AI adoption surges among small firms as labor substitution debate intensifies
A separate body of findings published by the Munich-based Ifo Institute illustrated the accelerating diffusion of artificial intelligence among smaller economic actors, particularly microenterprises and self-employed individuals. According to the data, AI adoption within this group rose sharply to 51.2 percent, up from 30.4 percent in 2025, while an additional 16.2 percent of respondents indicated plans to introduce such technologies into their business processes. As a result, their current adoption rate has approached that of the broader economy, which stands at 54.5 percent. Researchers emphasized that artificial intelligence can no longer be regarded as a purely future-oriented concept for these actors, but has instead already become embedded in routine operational and managerial workflows.
In terms of usage patterns, approximately 59 percent of respondents reported reliance on paid AI services, while 53 percent used free-of-charge tools, and around 7 percent operated self-developed applications, collectively indicating a pronounced dependence on externally provided systems rather than internally engineered solutions. Across functional domains, AI is primarily employed for tasks such as information retrieval, ideation, content production, translation work, and marketing-related operations, reflecting its role as a general-purpose productivity enhancer rather than a narrowly specialized instrument. In parallel, the Ifo “Jimdo business climate index” rose from -29.9 to -27.7 points in May, signaling a modest improvement in business sentiment among microenterprises and independent contractors. Nevertheless, despite this incremental stabilization, overall sentiment in this segment remains significantly weaker than that observed in the broader economy.
The potential effects of labor substitution within firms already deploying artificial intelligence were examined in another Ifo study. Approximately one-fifth of these companies reported that it is easy or very easy to replace employees holding academic or vocational qualifications with less skilled workers supported by AI tools, while roughly 15 percent expressed a similar assessment regarding the replacement of experienced personnel with less skilled but AI-enabled workers. These perceptions were most pronounced in the retail sector, followed by manufacturing, services, and construction, with only relatively minor variation observed across firm size categories. At the same time, a clear majority of organizations continued to report limited substitutability: 55.4 percent considered it difficult or impossible to replace degree-holding employees in this way, and 62.7 percent expressed the same judgment regarding experienced workers, underscoring the prevailing view that human expertise, particularly when accumulated through formal education and professional experience, remains difficult to fully replicate through artificial intelligence-assisted substitution.
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