AI is currently the dominant technology in the public debate. It will play a key role in the success of companies over the next five years. In a nutshell, this is the result of a recent survey of more than 680 C-level decision-makers that valantic conducted together with the Handelsblatt Research Institute.
The topic of artificial intelligence/applied AI plays a very important role in almost all companies and industries. What’s more: 70 percent of our survey participants report that the use of Applied AI has already achieved measurable business added value in the company. Financially, too: 36 percent confirmed an increase in profitability for valantic, 34 percent an increase in profits and 28 percent an increase in turnover. For the majority of respondents, the financial added value was between 10 and 39 percent, depending on the use case.
valantic has conducted numerous discussions and interviews in recent months. In general it can be said, and this should be noted in advance before going into the individual sectors: the business benefit that Applied AI creates stands and falls with the quality of the data. AI has a technical and an organizational side: data silos in the departments must be dissolved, consolidated in data warehouses and data lakes and cross-departmental collaboration strengthened. In the long term, a corporate AI strategy that is actively supported by management and C-level management promises the greatest success.
Opportunities and expectations
Importance of the following digital technologies for the company's success in the next five years*
* Proportion of corporate decision-makers surveyed who consider the respective technology to be "rather important" or "very important"; n=683. Source: Handelsblatt Research Institute / valantic (2025)
Due to the high investment volumes required, large retail chains such as Rewe and Penny in particular are experimenting with artificial intelligence.
“Artificial intelligence (AI) is nothing new for retailers and the Rewe Group,” says Christoph Eltze, Chief Digital and Technology Officer (CDTO). He is responsible for technical innovations on the Rewe Group’s Management Board. The retail giant has been looking into the opportunities and risks of artificial intelligence for years. “We were one of the first companies in the retail sector to publish an AI manifesto that provides specific recommendations for our developers when developing and using corresponding applications.” However, the Cologne-based company also wants to be at the forefront when it comes to the practical application of AI.
Computer vision (machine vision) uses intelligent camera technology to examine customer routes. This enables the efficiency of the sales area to be calculated, individual purchasing behavior to be analyzed and even theft to be detected.
Smart shelves – shelves equipped with sensors and cameras – use AI to record stock levels and customer interaction in real time. AI can also prevent out-of-stock situations through automated stock notifications. If a product is about to sell out, an alarm is sounded in good time. The AI system also rings the alarm bell if products are placed unfavorably on the shelf.
More information: Retail on the brink of AI change
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Artificial intelligence (AI) is changing processes in the fashion industry. Digital images are replacing human models. Customers design and order clothing with self-designed motifs.
Far-reaching new developments are on the horizon in the fast-moving fashion industry. “It is true that we are currently in the midst of a knowledge revolution in the fashion industry,” observed Prof. Ingo Rollwagen from the AMD Akademie Mode & Design in Berlin. The industry expert recognizes a profound change. It encompasses virtually all areas from design to collection development, clothing procurement and production, right through to marketing, sales and the purchase of clothing through e-commerce – “all of which are partly influenced by AI.”
The AI innovations in companies’ advertising are particularly striking. The Spanish fashion company Mango drew attention to itself with a remarkable campaign for its summer fashion. The clothing shown was real, but the model was an AI creation – and the digital image could hardly be distinguished from a real person.
How do a traditional Hessian sausage and artificial intelligence go together? Surprisingly well! The food industry is becoming increasingly digitalized, and AI plays a central role in this. From optimizing the maturing process to reducing food waste, AI can revolutionize quality and efficiency in production. A fascinating example: the “Ahle Wurst” sausage, whose traditional production process is refined with modern technology.
Information about the room temperature, humidity or pH value of the sausages is collected by sensors and transmitted to a central computer. A program precisely calculates the next steps. With the help of these specifications, the staff intervene in the maturing process. Feedback is then entered into the system and processed. In this way, the AI learns.
The Swiss food company Nestlé uses a similar approach at its plant in Osthofen in Rheinhessen. Here, the deep learning software “dStudio” from Sick AG ensures clear conditions in quality control in the “Health Science” division. The AI takes a close look at the cans of drinkable and buildable food produced there. Before the containers are filled, a dosing spoon is added for simple and precise portioning. During quality control, each individual can is checked to ensure that a spoon is actually present.
Artificial intelligence (AI) is helping the manufacturing industry to improve its processes, increase productivity and achieve a previously unattainable level of efficiency.
The production planning software of a specialist manufacturer of vacuum technology demonstrates what this can look like in a specific business scenario. The company manages 1,200 orders and has a planning period of six months. AI calculates the complete production plan with a total of 28,000 individual steps on 700 production machines, which takes just under 60 minutes on a standard PC. Compared to manual planning, this reduces the time required by around half.
One example of the use of learning AI in production machines is the disposal of chips. Chips are produced by abrasion during the machining of workpieces and are a frequent cause of machine downtime and malfunctions. The AI Chip Removal from DMG MORI uses artificial intelligence to analyze the amount of chips and then automatically disposes of them.
Two high-resolution cameras inside the machine are used for this purpose, which provide images of the work area. The system uses these images to analyze the chip volume in order to finally apply the optimum cleaning method. The coolant nozzles automatically adapt to the position of the chips and thus ensure that they are removed as quickly as possible.
The automotive industry is facing new challenges. “These include new drive technologies, materials from lightweight construction that are more complex to process and making production more flexible,” says Prof. Dr. Alexander Schiendorfer from Ingolstadt University of Applied Sciences. His field of teaching is AI-based optimization in automotive production.
“Artificial intelligence software tools are reaching an ever higher level of maturity,” says the AI expert. Supported by relevant data, AI can be used to carry out or completely automate numerous routine activities that were previously only possible with human hands.
The reward for the effort is efficient and resource-saving automotive production. Schiendorfer finds particularly suitable applications in the AI-supported forecasting of rejected parts, automated fault diagnosis and the optimized control of entire production processes.
In fact, intelligent process monitoring in production, for example in the quality-enhancing prediction of rejected parts or in predictive maintenance, is one of the most important topics that AI experts are working on in research and that is being tested in the automotive industry. Smart predictive maintenance reduces production machine downtimes to almost zero, resulting in better processes and more satisfied customers.
AI is on the verge of a major breakthrough in medicine. In Germany alone, AI applications in clinics and hospitals are expected to improve the quality of treatment by 30 percent and reduce costs by 20 percent by 2030, as PricewaterhouseCoopers predicts for inpatient healthcare. A study by Accenture predicts that AI and robotics could even save the healthcare industry around 150 billion US dollars worldwide by 2026.
One successful example of improved treatment techniques in medicine is image analysis. In large cancer treatment centers, radiotherapists are already using AI algorithms to mark tumor tissue. With this method, AI expert Lena Maier-Hein uses special spectral camera data to measure the blood flow in tissue, characterize it and differentiate it. The technology used achieves this far more precisely than the human eye ever could. Maier-Hein is a professor of computer-assisted medical interventions with a focus on surgical data science and computer-assisted biophotonics at the German Cancer Research Center (DKFZ). The results will soon contribute to a significant improvement in cancer diagnostics.
The results of an experiment at the University of Leeds are also very promising. The researchers have trained an AI system with retina scans and additional metadata to detect possible heart diseases from changes in the retina.
Will AI help us get better faster or even prevent us from falling ill in the future?
AI helps managers to make more successful business decisions. It turns figures into useful insights and bundles operational data into strategic statements.
Smart digital AI assistants will soon be supporting the management of a company. “Artificial intelligence will take over certain tasks from managers. Decision-making in particular is an essential task for managers, and in most cases these decisions are based on the evaluation and analysis of data and information,” emphasizes Prof. Dr. Tobias Kollmann, holder of the Chair of Digital Business and Digital Entrepreneurship at the University of Duisburg-Essen.
“The high level of acceptance of artificial intelligence at the top management level is astonishing,” says Andreas Renner, Academic Director of Steinbeis Augsburg Business School. Almost a third of top decision-makers even expect that decisions that deviate from the AI opinion should be justified. This should be disclosed to shareholders and government agencies.
The introduction of AI takes place in four steps with the help of a maturity model. The model determines the current level of development of a company division and describes a path from the lowest to the highest level. In this way, potential areas of application and optimization are gradually identified.
The valantic Digital 2030 Study
Find out which digital trends and technologies will shape the future. The valantic study “Digital 2030” highlights current developments and provides insights into how companies can use them for their success.
