The demand for AI-first strategies in Germany is extraordinarily strong. It is driven by the existential constraints of global competition and domestic challenges such as productivity stagnation and a shortage of skilled workers. However, this high demand is offset by considerable frictional losses due to regulatory, cultural and resource-related barriers. The transition from "AI as a tool" to an "AI-first" mentality is no longer a choice for the German economy, but a critical necessity for its economic sovereignty and future prosperity. This report argues that Germany's unique path lies in leveraging its industrial strengths and research excellence to pioneer trustworthy, high-value AI applications. This can turn potential regulatory burdens into a global competitive advantage. The analysis shows that the "need" is clearly defined; the challenge now lies in consistent implementation.
This part sets out the "why". It defines the concept of the AI-first strategy and outlines the powerful forces that make its adoption a strategic imperative for the German economy.
An AI-first strategy goes far beyond the use of isolated tools. It means a fundamental re-architecture of the company centered around data and intelligent automation.1 The core of this paradigm is the early and deep integration of artificial intelligence (AI) into all business processes to enable data-driven decision-making and unlock new efficiencies.1
Practical examples illustrate this change. It's not just about implementing a chatbot on a website, but transforming entire workflows such as legal investigations, software development or insurance claims processing through AI-powered assistants and so-called "digital knowledge workers".3 Companiessuch as OpenText demonstrate how AI assistants can summarize hundreds of pages, create onboarding kits in hours instead of weeks and automate 95% of test scripts in software development. The goal is to create a "limitless digital workforce" that augments human capabilities and allows employees to focus on higher-value, strategic tasks.3
This paradigm shift is also reflected in the realization that the introduction of AI must not be treated like a typical software implementation led by IT teams. A successful transformation requires the visible support of senior leadership, a holistic transformation of entire business units and the setting of clear financial targets to realize the full value of the technology.4
The urgency to pursue an AI-first strategy is driven by a convergence of external competitive pressures and internal structural challenges. These factors make AI transformation no longer an option, but a necessity for survival.
German companies explicitly state that strengthening competitiveness is a key benefit of AI deployment.5 There is widespread concern that Germany is falling behind the US and China in the global AI race, which are perceived as the dominant powers.5 According to a Bitkom study, 82% of industrial companies believe that AI is critical to competitiveness.8 The need to secure the country's economic performance in a challenging global environment is a primary motivator for strategic engagement with AI.9
AI is identified as a key lever to combat stagnating productivity in Germany.9 Studies cited by the Federal Ministry for Economic Affairs and Climate Protection (BMWK) predict significant annual productivity growth through AI-driven automation, with some estimates as high as 3.3% by 2030.9Thisis not a minor adjustment, but a fundamental economic catalyst. Data from PwC backs this up: Industries that are heavily impacted by AI are experiencing almost four times the productivity growth of less impacted industries.11
A key driver is AI's potential to drive innovation (cited as a key driver by 72% of companies12 ) and enable entirely new products, services and business models.6 Innovative companies are far more likely to be AI adopters, creating a virtuous cycle: innovation drives AI adoption, and AI in turn makes companies more innovative.9
The shortage of skilled workers (skills shortage) is a critical, two-pronged driver for AI adoption in Germany. On the one hand, AI is seen as a key solution to the severe labor shortage by automating routine tasks and freeing up existing employees.14 The goal is to maintain productivity despite the approximately 1.74 million unfilled jobs and free human workers from monotonous tasks.15
On the other hand, this very solution leads to a new, equally critical problem: a massive need for AI-specific skills.8 This creates a paradox: the technology that is supposed to close the skills gap cannot be fully implemented because the necessary skilled workers are not available. This interdependence means that a successful AI-first strategy in Germany is inextricably linked to a national education and training strategy. It underlines the enormous importance of user-friendly AI systems and the concept of "human-AI collaboration", where technology augments human capabilities rather than requiring in-depth technical expertise from each user.
The economic incentive for adopting AI-first strategies is significant and can be quantified at both macroeconomic and company level.
The market for artificial intelligence in Germany is on an explosive growth trajectory. According to forecasts, the market volume will increase from 4.8 billion euros in 2022 to around 10 billion euros by 2025 and further to 32.16 billion euros by 2030.19 This growth signals the immense economic value attributed to AI technologies.
At a macroeconomic level, studies cited by the BMWK suggest that AI could contribute between 0.4 and 3.4 percentage points to annual productivity growth over the next ten years.10 For the manufacturing sector, which is central to Germany, AI is forecast to generate additional gross value added of around 31.8 billion euros by 2028, which corresponds to around a third of the sector's total growth over this period.20
At company level, companies are reporting a tangible return on investment (ROI). Increased productivity is the most important metric (79%), followed by profitability (73%).8 One study shows that AI-using small and medium-sized enterprises (SMEs) are significantly more likely to have a positive net return on sales (76% compared to 46% for non-users).21 Thedemand for AI is thus shifting from a pure efficiency tool to an engine for innovation of core business models. While early drivers focused on process acceleration and cost savings, 16 more recent data from 2025 shows a strategic shift. Although efficiency remains important (84% of financial companies 23), the main drivers are now strategic in nature: securing future viability (59%), strengthening competitiveness (55%) 5 and enabling innovation (72%).12 This indicates a more mature understanding of the potential of AI. The "need" is no longer just operational, but deeply strategic and existential.
This part provides a data-based inventory of the current AI landscape in Germany and reveals a significant gap between strategic ambition and actual implementation.
The perception of the strategic importance of AI has increased dramatically in the German economy. An overwhelming majority of 91% of German companies now consider generative AI to be crucial to their business model - a massive increase from 55% in the previous year.12 78% see AI as an opportunity for their business.5
Despite this high strategic relevance, actual use is fragmented and varies depending on the study. The results indicate a gap between awareness and widespread implementation:
However, the strategic intention is underpinned by capital. 82% of companies plan to increase their AI budgets in the next twelve months, with 51% aiming for increases of over 40%.12 Another study confirms that 78% of companies plan to increase their AI investments.8
The different figures on "adoption" make it clear that it is not a single, universally defined metric. Rather, they highlight the gap between strategic planning and widespread operational implementation. The following table summarizes the results of several recent studies and provides a nuanced perspective.
|
Metric |
KPMG (Q1 2025) |
Deloitte (Q4 2024) |
Bitkom (Q1 2025) |
IW Cologne (2024 data) |
ifo (2024 data) |
|
Sees AI as business-critical |
91% 12 |
- |
78% 5 |
82% 8 |
- |
|
Active AI use |
~50% (widespread) 12 |
23% (daily GenAI use) 24 |
20% 5 |
37% 9 |
27% 10 |
|
Has an AI strategy |
69% 12 |
85% report progress 8 |
- |
- |
- |
|
Plans to increase investment |
82% 12 |
78% 8 |
36% 8 |
- |
- |
There is a clear gap in AI adoption that depends on the size of the company. Large companies with more than 2,000 employees have an adoption rate of 48%.8 A separate study even shows that 75% of large companies (more than 250 employees) use AI.21 In contrast, usage among medium-sized (28%) and small companies (17%) is significantly lower.8
This data points to a dichotomy in the German SME sector. There is an elite group of "AI champions" - probably the often-cited "hidden champions" 21 - that are aggressively pursuing AI-first strategies and are even leading the way in Europe. At the same time, a much larger proportion of SMEs remain hesitant or do not have the necessary resources. A study by Sage paints a more optimistic picture here, noting that 29% of German SMEs have already fully
have alreadyfully integrated AI, which is slightly above the EU average of 28% and makes them European pioneers in this segment.27 This suggests that the SMEs that decide to adapt are doing so with great depth. This finding has far-reaching implications: Political and economic support measures cannot follow the watering can principle. They must differentiate in order to both help the laggards catch up and strengthen the champions in global competition.
The most common areas of application for AI in German companies are IT (75%), marketing and sales (64%) and customer service (59%).8 In an industrial context, the primary area of application is
production and service provision, where 45.5% of AI-using companies use the technology for tasks such as quality control and predictive maintenance.9 This is followed by IT (37%) and marketing (35.2%).9
The main objectives pursued with the use of AI are the automation of routine tasks (84.5%), support for complex tasks (70.1%) and improving quality (64.6%).9
In an international comparison, Germany presents a mixed picture.
This pattern points to a profile of the "cautious pioneer". German companies may be slower to start, but when they do engage, they focus on sophisticated, high-value applications rather than superficial implementations. This is in line with the national focus on engineering excellence and complex B2B solutions. It's less about AI for the mass market and more about deep industrial integration.
This part analyzes the main obstacles that explain the gap between ambition and reality and form the core of the strategic challenge.
The EU AI Act, which came into force in August 2024, is the most significant regulatory factor for AI adoption in Germany.28 It establishes a risk-based approach that imposes strict requirements on "high-risk systems", which are widely used in industries such as finance, human resources and critical infrastructure.30
This regulation is perceived ambiguously. On the one hand, it represents a considerable hurdle. A majority of German companies cite legal uncertainty (82%), data protection requirements (73%) and regulatory hurdles (68%) as the biggest challenges.5 52% of German managers feel actively restricted by regulation - more than in any other country surveyed.4 This can cripple innovation, especially for SMEs that lack the resources for complex compliance.32
On the other hand, regulation is a central part of the "AI Made in Germany" value proposition. It aims to create a global seal of approval for "Trusted AI".8 By ensuring transparency, safety and ethical standards, German companies can gain the trust of customers and gain a competitive advantage in markets where these factors are valued.28
The EU AI Act thus forces German industry to make a strategic decision: either compete on the basis of "trust" or risk falling behind on "speed". The regulation creates significant compliance costs and potential development delays, particularly for high-risk applications that are prevalent in key German industries (finance, automotive, medical).31 This contrasts with less regulated environments in the US and China, which allow competitors to move faster and potentially more cost-effectively.5 The explicit goal of the German and European strategy is to leverage this regulation to create an "AI Made in Germany/Europe" brand that stands for safety, reliability and ethics.33 The success of German companies therefore depends on their ability to market this "trust premium" effectively. They are betting that global B2B customers will be willing to pay more or wait longer for a certified, trustworthy AI solution. This is a high-risk, high-reward strategy that defines Germany's unique position in the global AI race.
The lack of technical talent and in-house expertise is one of the top three barriers cited in several studies.8 60% of German companies report a lack of in-house AI expertise.18 The problem is exacerbated by a "brain drain" of top talent to the US and Switzerland.36
However, the need goes beyond pure development skills. Companies need employees with skills in strategic thinking, data analysis and human-AI collaboration.37 The ability to critically evaluate and validate AI results is becoming increasingly important, as 56% of employees say they make mistakes based on unverified AI results.8 So this "talent gap" is not just a shortage of programmers, but a shortage of "AI literacy" across the workforce. The future of work is described as "human-AI collaboration ", requiring new skills that focus on interaction, validation and the strategic application of AI, not just its creation.
Germany is responding with national strategies for lifelong learning and continuing education (Nationale Weiterbildungsstrategie).42 The government is funding new AI professorships, integrating AI into university curricula and modernizing vocational training to teach AI skills.45 Yet a gap remains: While 64% of workers are interested in AI training, many companies lack concrete programs.12 This implies that simply hiring more data scientists is not enough. The "need" is for a broad-based training initiative that makes the entire workforce - from the factory floor to the executive suite - AI-competent.
In addition to regulation and a shortage of skilled workers, cultural and infrastructural factors are also slowing down AI adaptation. Compared to international competitors, German companies are often characterized as more cautious and risk-averse.4 This attitude can slow down the introduction of disruptive technologies such as AI.
A fundamental prerequisite for any AI-first strategy is a solid data foundation. The biggest challenges include a lack of high-quality, accessible data, an inadequate data infrastructure and a lack of trust in data exchange with partners.35 This is precisely the problem that initiatives such as GAIA-X and Catena-X aim to solve.54
Finally, an internal trust deficit is also a significant cultural barrier. Only 46% of employees trust AI systems, and 66% use AI results without checking them for accuracy, leading to significant error rates.8 Thislack of trust hinders effective human-AI collaboration.
This part breaks down the general trends into the specific contexts of Germany's key economic sectors and shows how the need for AI-first strategies varies by industry.
The manufacturing sector is a pioneer in AI adoption in Germany. 42% of industrial companies use AI in production.56 Production is the most common area of application for AI across all sectors (45.5% of users), with a focus on predictive maintenance, quality control and process optimization.9 The main drivers are increasing efficiency, optimizing resources and addressing the shortage of skilled workers on the factory floor.20 The Green-AI Hub provides concrete examples, such as the use of AI to optimize plastic molding processes to increase the amount of recycled material 57 or the use of acoustic signals for predictive maintenance of machines.35
The automotive industry is a key driver of AI in Germany, particularly in the areas of research and development and production.58 Germany is a leader in patents for autonomous driving.59 Applications span the entire value chain, from supply chain management to in-vehicle user experience.60 Keydrivers include supply chain resilience, product carbon footprint (PCF) tracking, quality management and the creation of new digital services.54
The Catena-X lighthouse project is creating a secure, sovereign data ecosystem for the entire automotive value chain, based on the principles of GAIA-X. It aims to standardize data exchange for use cases such as quality management, logistics and carbon footprint tracking, thus also enabling SMEs to participate.53 Such industry-specific data ecosystems are Germany's most promising response to the global platform economy. German industry, especially SMEs, is struggling with data silos and a lack of trust in data exchange.53 While global competitors are dominated by large, centralized platforms, Germany cannot and does not want to replicate this model due to its industrial structure and values (data sovereignty). Catena-X represents a federated, decentralized alternative. It is not about one company owning the data, but about creating common standards and a trustworthy framework for collaboration across an entire industry. This approach plays to Germany's strengths: B2B cooperation, industry standards (DIN) and a strong industrial base. If successful, this "Manufacturing-X" model could become a blueprint for other sectors and create a competitive advantage based on collaborative data use rather than monopolistic data sovereignty.
The financial sector has a high adoption rate: 73% of companies use at least one AI use case.22 Insurance companies(78%) are slightly ahead of banks (71%).22 Key applications can be found in operations (process automation), risk management and compliance.17 The main drivers are efficiency gains (84%) and cost savings (63%), reflecting the industry's high-pressure environment.22 This sector is strongly affected by the EU AI Act, as many applications (e.g. credit scoring, insurance premiums) are classified as "high risk".31 Consequently,regulatory requirements are among the biggest obstacles.22
Although the overall adoption rate in SMEs is lower than in large companies, SMEs using AI are highly innovative and successful.21 57% of AI-using SMEs are innovators, compared to only 24% of non-users.21 They often start with accessible tools (Level 1 and Level 2 AI) for copywriting, marketing or integrated software functions.65 For SMEs, the main drivers are efficiency, pressure to innovate and overcoming resource constraints.27
The traditional success factors of the "hidden champions" - deep focus, customer-centric innovation and globalization 26 - are reinforced by AI. AI enables them to analyse customer needs more precisely and optimize their niche production processes.35 Case studies show successful AI applications in SMEs, such as predictive maintenance (INDIA-DREUSICKE 35), the optimization of warehouse layouts (HARTING 35) and the automation of production steps (Eifelbrennholz 35).
|
Industry |
Adaptation rate |
Primary use cases |
Key initiative / challenge |
|
Industry 4.0 / Manufacturing |
42% 56 |
Predictive maintenance, quality control, process optimization, resource planning 9 |
Green AI hub 57 |
|
Automotive |
High (esp. R&D), 39.9% in "Vehicle construction" group 9 |
Supply chain management (PCF, traceability), autonomous driving, in-car assistants 59 |
Catena-X 53 |
|
Financial services |
73% 22 |
Risk management, compliance, process automation, fraud detection 17 |
EU AI Act Compliance for high-risk systems 31 |
|
Medium-sized companies |
Lower than large companies, but profound among users 21 |
Accessible tools (text, marketing), process automation, specialized niche solutions 35 |
Scarcity of resources, lack of expertise 35 |
This part assesses the fundamental pillars that support or hinder Germany's path to an AI-first economy.
The national AI strategy, which was launched in 2018 and updated in 2020, aims to establish "AI Made in Germany" as a global seal of quality.33 The German government increased the committed funding from 3 billion euros to 5 billion euros by 2025.45 The strategy focuses on strengthening research (over 100 new AI professorships), promoting transfer to the economy (especially SMEs), developing talent and creating a regulatory framework that promotes trust.42
Critics complain that the strategy portrays the emergence of AI as an unalterable, autonomous development and possibly neglects policy options.69 While research funding is strong, the crucial transfer to industry (patents, start-ups) remains a weak point.29 The success of the strategy will be measured by whether it succeeds in closing this gap between research and the market.
GAIA-X is the fundamental project to create a federated, secure and sovereign European data infrastructure.55 Its aim is to provide a competitive alternative to proprietary cloud platforms in order to avoid vendor lock-in and ensure compliance with EU data protection laws.55 It provides the trust framework and technical standards for data spaces.
As the first major industrial use case for GAIA-X, Catena-X applies these principles to the automotive value chain.54 It demonstrates how a data room can solve real-world business problems such as supply chain transparency and carbon footprint tracking in a collaborative multi-stakeholder environment.62 The success of these data rooms depends on establishing clear governance, ensuring interoperability and creating strong economic incentives for participation. The biggest challenge is convincing data owners that the benefits of participation outweigh the risks and costs.70
Germany's research institutions (Fraunhofer, DFKI, Max Planck, etc.) are leaders in several next-generation AI fields that fit perfectly with the "Trusted AI" strategy.
These fields of research are not isolated endeavors. They are a direct strategic response to the challenges posed by the EU AI Act and energy shortages. The EU AI Act requires transparency and human oversight for high-risk systems 30; XAI provides the technical means to meet these legal requirements.78 The massive energy consumption of large AI models is a significant economic and environmental problem 73; Green AI research at institutions such as Fraunhofer and DFKI 74 addresses this problem directly. These research areas are thus creating the fundamental technologies that make the vision of "trustworthy, sustainable AI made in Germany" technically and economically feasible.
This final part translates the analysis into actionable recommendations and offers a concluding vision for Germany's AI future.
The need for AI-first strategies in Germany is intense and unavoidable, driven by the twin pressures of global competition and domestic structural challenges. Germany will not overtake the US and China by copying their consumer-centric, centralized platform models.
Its unique path to leadership lies in a federated, B2B-focused approach that leverages its industrial heritage, engineering prowess and research excellence. The ultimate success of "AI Made in Germany" will depend on the nation's ability to translate the principles of trust, safety and sustainability - codified in its regulations and pursued in its research labs - into a tangible and globally sought-after value proposition. The need is clear; the challenge now lies in consistent and bold implementation.