AI maturity in organizations in Germany

Current AI adoption

Organizations in Germany take a varied approach to AI, with its maturity differing from other European markets. This is likely due to the late rollout of AI initiatives in the country, leaving many organizations in the learning and adoption phase of their AI journey as they determine what works best for them. Initial AI initiatives were rolled out in Germany in 2018, similar to other European markets, but few German organizations were embracing AI adoption. Instead, Germany re-issued the initiatives in 2020 to focus on further establishing and strengthening the application of AI more broadly, which led many to dedicate efforts to AI implementation post 2020. However, due to the two-year gap, some organizations are further advanced, and some have slipped behind in the AI journey, meaning there’s a contrasting population in Germany for AI (see Figure 1).

This uneven adoption of AI across German organizations has led to differing levels of preparedness, particularly when it comes to foundational elements like data management and strategic direction. While some organizations are advancing rapidly, others are still establishing the infrastructure and processes needed to support AI initiatives. This divergence is evident in how organizations approach AI readiness, with notable gaps in data preparation and strategic alignment that may be limiting their ability to scale AI effectively.

Fewer organizations in Germany identify key actions for accelerating AI adoption when compared to the global average. For example, only 38% highlight the need to source and prepare relevant high-quality data for AI training and analysis (global average of 48%), 33% want to prepare unstructured data for AI model training and applications (vs 43% globally), and just 39% are looking for a clear vision, strategy or direction for AI initiatives (47% globally). Perhaps this lower emphasis on foundational readiness is what’s causing such inconsistency across the country. What’s more, this discrepancy is also reflected when looking at AI strategy and adoption journeys. Around a quarter of German organizations (23%) are in the early stages of defining an AI strategy, whether that’s still defining their strategy (8%) or having a defined strategy that has not yet been implemented (15%).

However, a lack of AI strategy isn’t affecting all organizations. In fact, a similar proportion to the global average (Germany: 24% and Global average: 25%) have a defined AI strategy that has been implemented organization wide. The differences in AI journeys likely come from a combination of many external factors such as misalignedleadership and implementation, a skills gap or growing caution to regulations. Those that have expert skills are able to push ahead into AI maturity, but those lacking guidance are struggling to make their next move.

There’s also a need for wider guidance in Germany. The current state of preparation and strategy in the region risks leaving many German organizations behind in the global AI race. Without investing in foundational elements such as AI training, data preparation, and clear AI strategies, these organizations may struggle to scale their AI initiatives or realize the full potential of AI-driven transformation. Addressing these gaps will be critical for Germany to unlock the broader benefits of AI across industries.

Fewer in Germany are using AI for customer service areas than the global average. Similarly, fewer are using AI across marketing, human resources, sales or customers facing products (see Figure 2).

However, customer engagement is still a priority as 97% of German respondents say their organization effectively uses AI-powered agents to streamline operations and improve customer experience. This indicates that while direct application in services areas may be lower, AI is still playing a role in improving customer interactions. With strict data privacy laws outlined in the Conference of Independent Federal and State DataProtection Supervisory Authorities, and a cautious approach to new technologies, German organizations might hesitate to deploy AI in customer-facing areas, where compliance risks and public trust are more critical.

Instead, German organizations are most likely to be focused on using AI for IT and security (Germany: 86% vs global average: 82%) and research and development (Germany: 56% and global average: 53%) as their most common uses. German businesses may prioritize back-end operations over customer-facing applications due to a strategic focus rather than customer engagement.

This approach may be Germany’s solution to ensuring customer-facing touchpoints aren’t overlooked. By balancing their focus on AI for operational efficiency with AI for customer engagement, these organizations could unlock additional value and adopt a more holistic approach to AI-driven success.

While nearly two-thirds (63%) of German organizations are focused on using AI to help reduce costs by cutting overhead expenses and time spent on non-revenue-generating tasks, they are far less likely to believe AI’s primary use cases revolve around streamlining operations (29% vs 47%) or optimizing processes through automation (39% vs 57%).

This disparity raises the question: could German organizations perceive themselves as being more AI mature than they truly are, prioritizing complex use cases over quicker wins that could accelerate their AI success? By re-evaluating their priorities, German businesses could potentially achieve a more balanced and impactful approach to AI implementation.