Ten minutes—that’s the time it takes to clearly document a malfunction in a facility, ensure a customer email is easy to understand, or prevent the shift log from turning into a novel again. It’s also the time when AI is often truly useful: brief and to the point. It is precisely this reality that can now be quantified for East Westphalia-Lippe (OWL). A corporate survey (2021 versus 2025) and a resulting working paper from the it’s OWL Arbeitswelt.Plus project show that the median daily AI usage time has risen from zero minutes to ten minutes. At the same time, many companies are spending more time on the introduction and piloting of AI.

AI tools increasingly relevant

The ten minutes do not stand alone. They are supported by a second metric: The proportion of respondents who “never” use AI drops significantly from 29 percent (2021) to 5 percent (2025). In the same vein, the working paper interprets this trend as an indication of greater integration of AI tools into everyday work processes and speaks of the “increasing operational relevance” of AI in companies. This is a sobering but important insight. When people talk about AI, they quickly resort to big words: disruption, productivity leap, new business models. The survey from OWL shows instead: AI is being used more frequently, but in small doses. This fits the industrial workplace, where effects often accumulate through many small steps in processes.

From Discussion to Testing: Piloting Grows, Adoption Only Slowly

When asked at what stage companies are using AI, the picture of corporate responses shifts significantly between 2021 and 2025: planning and discussion drop from 40 to 23 percent, introduction and piloting rise significantly from 26 to 37 percent, and application and deployment grow from 21 to 25 percent. The results suggest that while companies make progress after discussion and planning, they linger in the piloting phase for an extended period. Furthermore, the share of companies with no AI activities remains constant at 15 percent. In other words: the initial adoption is succeeding more frequently. Scaling remains the bottleneck. This is precisely what makes the ten-minute results so interesting: they show that AI is finding its way into everyday use. But they do not yet guarantee a rollout, standards, or reliability.

Text Instead of Automation: Why Generative AI Is Delaying Adoption

The working paper also offers insight into why AI is becoming so visibly integrated into everyday life right now: Since the emergence of generative AI applications like ChatGPT, automatic text generation in particular has gained significance. ChatGPT is described as a turning point; automatic text generation and natural language processing are gaining importance, while several characteristics associated with traditional automation have been declining statistically significantly since 2021.

More usage, more friction: AI adoption remains complex

Anyone expecting that growing usage will automatically lead to fewer problems will be disappointed by the paper. Feedback on challenges shows that dealing with AI remains complex. Compared to 2021, a greater number of factors are perceived as problematic. The issues concern not only technical and infrastructural matters but also legal, ethical, and organizational aspects. The paper describes a persistent ambivalence: more adoption, but demanding requirements.

Long implementation phases and complexity as key factors

This also aligns with the piloting logic: those who test AI encounter questions more quickly that cannot be resolved with a tool update. Governance, responsibilities, data security, training, and team acceptance—the working paper explicitly cites long implementation phases and complexity as key factors and links this to the particular complexity of AI applications.

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