The use of artificial intelligence is particularly advanced in the automotive industry.
(Photo: imago images/Science Photo Library)
Berlin, Dusseldorf The proportion of companies in Germany that actively use artificial intelligence (AI) exceeded the ten percent mark last year. Compared to 2019, the number of companies using AI has almost doubled. This is shown by a current survey by the Leibniz Center for European Economic Research (ZEW) for the Federal Ministry of Economics, which is available to the Handelsblatt.
The institute also surveyed 550 AI-active companies more closely about the use of the technology around the turn of the year. Accordingly, use is particularly widespread among information and communication service providers, financial service providers, engineering offices, management, legal and tax consultants and advertising agencies. In industry, the highest proportions are in the electrical industry, vehicle construction and mechanical engineering.
“The topic is hot,” says Sabine Laukemann, board member at the medium-sized IT service provider Datagroup from Stuttgart. “We get asked about it by customers from all industries.”
So far, the main areas of application of AI have been speech recognition, the automation and optimization of processes, machine learning and what is known as text mining, in which texts are evaluated with software. “AI is not only about automation, but also about expanding and multiplying the business model,” says Laukemann. “It used to be a specialist issue, but today it concerns everyone.”
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The automotive industry shows the central role that AI plays. There, artificial neural networks, which are based on the human nervous system, are an important part of assistance systems and autonomous driving systems in vehicles. The large amount of information from cameras, radar and lasers is processed by an AI in the car that makes vital decisions.
Electric car pioneer Tesla relies more radically on AI than any other car manufacturer. To do this, the company is developing what is known as computer vision, which recognizes objects in traffic with high speed and precision so that the car can drive itself. Tesla CEO Elon Musk recently announced he would hold an “AI Day” in August to “attract new talent to Tesla.”
Many companies are still in the early stages
Overall, the ZEW attests that German companies have a “quite high level of technical skills and that the topic of AI is well anchored in the organization”. The willingness to cooperate with scientists or other companies is also “extraordinarily high”.
However, most companies have only just gotten into the subject. Around a fifth of the surveyed AI-active companies are currently “in the experimental stage”, and another two-fifths have at least developed the first use cases. This shows how quickly AI is spreading in the economy. Only in every seventh company that uses AI does it already form the basis for the company’s business model.
The ZEW demands that politicians must quickly improve the framework conditions in order to provide support: they must ensure that AI is included in the curricula in schools and universities. And because many companies have deficits, especially in non-technical areas, business administration and law also have to be taught what AI can be used for. In addition, a more comprehensive range of advisory services is required.
Further prerequisites for AI to experience a breakthrough in the economy are the establishment of a cloud infrastructure and the development of standards, the nationwide expansion of a powerful IT infrastructure and legal regulations to improve access to and the possibilities for using data.
Use of AI is often driven by bosses
So far, bosses in particular have been pushing the use of the technology. “In-house AI teams and the development of AI use cases in all areas of the company can only be found in a minority of AI-active companies,” says ZEW innovation expert Christian Rammer. In addition to proactive management, these are “often a prerequisite for the high effectiveness of the use of artificial intelligence”.
In detail, most AI-active companies also have in-house employees with skills in handling data and developing software solutions. On the other hand, methodological skills for machine learning and the explainability of AI results are less widespread.
More: The brain of Tesla – this is how the electric pioneer works on artificial intelligence