Integrating AI with real-world systems: robots, machines and vehicles

The debate surrounding artificial intelligence continues to be heavily influenced by digital applications, from chatbots to data analysis. The real structural change, however, is taking place in the physical world. In this context, we refer to “Physical AI”, that is, the fusion of artificial intelligence with real-world systems such as robots, machines, or vehicles. This development brings AI from the virtual into the real world, thereby marking a decisive next step in technology.

Applications that go far beyond traditional automation

This integration opens up a wide range of applications that go far beyond traditional automation. Whereas industrial robots were previously used primarily for clearly defined, repetitive tasks, the combination of improved sensor technology and adaptive algorithms now enables entirely new capabilities. Machines are increasingly able to perceive, interpret and respond to their environment independently – for example, through autonomous navigation or AI-supported quality control.

Automation and AI as an industrial reality

The momentum behind this development is considerable. Several million industrial robots are now in use worldwide, with over half a million new systems being installed each year. Automation and AI are therefore no longer a topic for the future, but a widespread industrial reality.

This transformation is particularly evident in the industrial sector. In automotive production, new generations of production systems are emerging in which robotics no longer functions as an isolated tool, but as an integral part of intelligent processes. At the same time, the convergence of AI and physical systems is also evident outside traditional manufacturing environments, for example in autonomous vehicles. Waymo, a subsidiary of Alphabet, is already operating a fully autonomous robo-taxi service in several US cities such as San Francisco, Los Angeles, Phoenix, Dallas, and Houston. The vehicles use AI to navigate complex traffic situations and transport passengers safely to their destinations. The service currently records around 500,000 paid journeys per week, with the number of passengers having doubled within a year. Expansion into over 20 further cities worldwide, including London and Tokyo, is planned by the end of 2026.

A particularly key area of application is autonomous production facilities in the context of Industry 4.0. Here, the concept of “predictive maintenance” plays an essential role. AI systems monitor machines in real time, detect signs of wear at an early stage, and schedule maintenance work before breakdowns occur. This predictive maintenance reduces costs and minimises production downtime. Companies such as Siemens use IoT (Internet of Things) solutions for this purpose to continuously analyse sensor data such as vibration, temperature, or pressure. Machine learning algorithms identify patterns that indicate potential defects – such as bearing failures or motor overheating. Ideally, such measures can reduce downtime by up to 50 %.

In logistics, the integration of AI into physical systems is particularly evident. Tens of thousands of autonomous robots are already at work in Amazon’s logistics centres, including so-called “Kiva” robots, which transport shelves, pick goods, and prepare them for dispatch. AI controls the route planning and coordination of the systems to ensure maximum efficiency. The robots navigate through the warehouses using 2D barcodes on the floor and can move shelves weighing up to 340 kilograms at a speed of around 5.5 km/h. Amazon acquired Kiva Systems in 2012 for approximately USD 775mn. In addition, specialised robotic solutions such as “Stretch” from Boston Dynamics – now part of Hyundai – are being used to autonomously move and stack pallets in warehouses. The underlying AI enables the robots to recognise objects, plan movements, and adapt dynamically to their surroundings.

In agriculture, too, the combination of AI and physical machinery is bringing about far-reaching changes. Autonomous tractors from John Deere use computer vision to work the fields independently – from ploughing and sowing right through to harvesting. The systems detect obstacles, optimise routes, and adapt work processes to soil and weather conditions. At the same time, the concept of “precision agriculture” is gaining in importance, whereby resources such as water, fertiliser, pesticides, and seeds are used in a targeted and efficient manner. Technologies such as “See & Spray” make it possible to distinguish weeds from crops with precision using AI and cameras, and to apply herbicides only where they are actually needed. This can reduce the use of chemicals by up to 90 %.

In the medical sector, too, the integration of AI into physical systems is advancing continuously. While (semi-)autonomous surgical robots such as the “da Vinci” system from Intuitive Surgical are still controlled by surgeons, AI algorithms support the precision of the procedures. For instance, the technology analyses real-time image data, filters out tremors from movements, and warns of sensitive structures such as nerves or blood vessels. In addition, service and assistance robots are being deployed, such as Toyota’s “Human Support Robot” (HSR), which supports carers in looking after patients by, for example, fetching medication or helping to reposition them in bed. Therapeutic applications are also gaining in importance: the therapy robot “Paro”, developed in Japan and modelled on a small harbour seal, is used in particular in elderly care, for dementia patients, in psychiatry, and in the field of autism to stimulate emotions, reduce stress, and promote social interaction.

(c) APA-Images / dpa / Waltraud Grubitzsch

Enormous market potential by 2030

A look at market trends underscores this assessment. As the following Statista infographic shows, the market for AI robotics is currently still at a relatively early stage but is demonstrating an exceptionally strong growth momentum: starting from a market volume in the mid double-digit billions, the market is expected to expand many times over by 2030. This growth is being driven by both industrial applications and service robotics, with software and AI-based solutions in particular showing disproportionately high growth. Please note: forecasts are not a reliable indicator of future developments.

From a macroeconomic perspective, studies underscore the potential of this development. Even a moderate increase in the use of AI can significantly boost corporate productivity – by more than 14 % in some analyses (source: Das KI-Magazin, Opens In New Window). At the same time, the introduction of such technologies is evidently not without friction. Indeed, transformation initially involves friction before efficiency gains are realised. Short-term productivity losses are common before the long-term benefits take effect. There is also potential for socio-political conflict, given the many concerns regarding the impact on the labour market.

Physical AI: a long-term growth driver for the capital markets

Despite these challenges, growth momentum remains strong. The drivers are not only efficiency gains, but above all structural factors such as a shortage of skilled workers, rising quality requirements, and the increasing interconnection of production systems in the context of Industry 4.0. From an investor’s perspective, this makes it clear that the greatest opportunities arise across the entire value chain – from hardware and software right through to industrial end-use applications. In future, the actual value creation will take place at the interface between digital intelligence and physical implementation. This is precisely where Erste AM Senior Fund Manager Bernhard Selinger sees the structural winners of the coming years. For him, “Physical AI” is therefore no longer a future scenario, but an ongoing transformation process – and one of the key long-term growth drivers for the capital markets.

 

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