That's why, after years of training with predominantly static data such as texts, images and simulations, AI is leaving the laboratory to get to know the dynamic world outside. A current example is the strategic partnership between Bosch and the robotics start-up Neura Robotics.

The aim of this cooperation between the two German companies is to bring humanoid robots into industrial environments in order to generate physical training data that goes beyond static simulations. The AI-controlled robot is thus doing a kind of internship at the workbench. This is because there is growing pressure worldwide to provide AI models with real-world, embodied experiences. Today, decision-makers in industry see robots not only as actuators, but also as data suppliers for adaptive systems that must master autonomous adaptation, causal understanding and physical interaction in order to reach the next level of automation.

Learning ability as a key growth driver

The industry has recently made significant progress in this context. Globally, investment in industrial robotics is reaching new record levels, while physical AI – a combination of AI and real physical interaction – is increasingly coming into focus. According to the International Federation of Robotics reports on the top 5 trends for the robotics industry for 2026, published a few days ago, the market value for industrial robotics installations has increased to an all-time high of 16.7 billion US dollars, with the use of adaptive machines being the key growth driver. At the same time, venture capitalists are investing large sums in companies that are transferring AI into the physical world, accelerating the development of adaptive automation systems.

AI learns from real actions and consequences

In the current example from Germany, the strategic partnership between Bosch and the robotics start-up Neura Robotics, this means that sensor suits will be used to collect and evaluate movement and production data directly in factories – a decisive step towards AI that not only reacts but also learns from real actions and consequences.

Competence for new tasks in 48 hours

Internationally, examples such as the development of the humanoid robot Atlas by Boston Dynamics are also making headlines. The company's CEO recently emphasised that this platform will be used in a Hyundai factory in the future and must be able to learn new tasks in 48 hours in order to cope with the diversity of industrial processes. This shows that autonomous adaptability and real experience beyond predefined routines are now being seriously pursued for the practical use of robots in factories.

Deterministically programmed robotics is reaching its limits

What does this mean for decision-makers in industry? Firstly, the trend shows that conventional, deterministically programmed robotics is reaching its limits. The physical world is complex, dynamic and characterised by uncertainties – classic automation systems quickly reach their performance limits. Access to data generated from physical interactions – such as gripping processes under varying conditions, force and contact information, or adaptive navigation – is a decisive competitive advantage. Without these real-world experiences, AI models inevitably remain superficially trained and less robust in the face of variability in production environments.

Combining simulation training with real-world experience

In addition, users in industry know that simulations alone are not enough. Although virtual environments can generate large amounts of data and accelerate initial learning phases, they cannot replace the nuances and unpredictability of the real world. Combining simulation training with real-world experience – captured by robot systems that actively act and learn – is now a valid paradigm for robust industrial AI.

Physical experience as a core asset

For industrial decision-makers, this means setting a strategic course: the integration of adaptive robots is not just a question of technology, but also of data strategy. Companies must build platforms and processes that use physical experience as a core asset to continuously improve AI models. This includes investments in sensor technology, data infrastructure, security and compliance processes, and the organisational ability to systematically extract knowledge from real-world interactions.

Learning production systems that are flexible, resilient and scalable

In the short term, the benefits lie in efficiency gains and productivity increases through more adaptive automation. In the long term, the ability to use robots as data-providing actors paves the way for learning production systems that are flexible, resilient and scalable – and thus for a key competitive advantage in an increasingly AI-dominated industry.