Job killers or job savers? The truth about automation, AI, and robotics – From the assembly line to the “thinking line”? – Image: Xpert.Digital
Smart Factory: Challenges and solutions on the path to intelligent production
From assembly line to “thinking line”: AI robots are changing the rules of the game in industry
Industrial production is undergoing a period of profound transformation. New technologies such as artificial intelligence (AI), robotics, and automation promise far-reaching changes in virtually every sector, from manufacturing and logistics to healthcare and retail. Many decision-makers are aware of the immense potential of these technologies and view AI, robotics, and automation as the keys to the future. At the same time, practical experience shows that significant hurdles still need to be overcome before intelligent production and process chains can become widespread.
The following section examines the obstacles to intelligent production, how companies can successfully overcome these challenges, and which trends and developments will shape the future of AI, robotics, and automation. The focus is on a well-founded and understandable presentation: the aim is to highlight the most important aspects, explain the necessary technical terms, and derive practical recommendations.
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1. Potential and importance of AI, robotics and automation
Revolutionary technologies for competitiveness and growth
Companies are increasingly engaging with AI systems, robotics, and automation because they expect significant productivity gains, lower costs, and greater competitiveness. Concrete results can already be observed in many areas: AI-supported systems, for example, take over complex analyses, identify sources of error in production processes, or enable predictive maintenance of machines. Robots can take over monotonous, physically demanding, and potentially dangerous tasks, while automated processes optimize the efficiency of entire supply chains.
Practical examples
- Logistics: Autonomous mobile robots (AMRs) are used in warehouses to pick or transport goods. This increases efficiency and relieves the workload of employees.
- Manufacturing: Collaborative robots (cobots) work side by side with humans and enable flexible adaptation of production steps.
- Service sector: AI systems can process customer requests, use automated chatbots to answer questions, and thus improve customer service.
- Healthcare: Robots are used in surgeries or rehabilitation, while AI applications can assist doctors in diagnosis.
These examples illustrate the wide range of applications. However, despite these positive prospects, numerous challenges arise that hinder widespread use.
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2. Key obstacles and challenges
Safety concerns and regulatory requirements
Businesses and the public often approach new technologies with caution. Safety concerns play a central role: when robots work directly alongside humans, accidents must be prevented. This is especially true for collaborative robots (cobots) that share workspaces with employees. Even the slightest incorrect movements can have potentially serious consequences, which is why these systems are often equipped with additional sensors, automatic stop mechanisms, or safety devices.
“Companies must invest in robust security concepts so that AI systems and robots comply with applicable security standards,” is a demand frequently heard from industry and research. Furthermore, many sectors are subject to strict regulatory requirements, ranging from data protection to product liability. Particularly with AI applications, it is unclear how to address liability issues when a learning system makes an incorrect decision. Legislation must be adjusted promptly to establish clear frameworks.
High costs and lack of funding
A significant hurdle remains the cost. Developing and implementing AI solutions, as well as robotics and automation solutions, involves substantial initial investments. This begins with hardware, such as sensors and actuators, extends to robotics platforms, and includes highly specialized components like lidar or powerful processors. Software development represents an additional cost factor: AI algorithms sometimes need to be custom-designed and trained for specific use cases, requiring qualified specialists and expensive computing resources.
For small and medium-sized enterprises (SMEs) in particular, the financial burden is often a major hurdle, especially since the precise return on investment (ROI) for AI projects cannot always be accurately determined in advance. However, there are ways to circumvent these problems:
- Cloud services: Cloud-based AI services allow companies to flexibly rent computing power and storage space, thus avoiding high hardware costs.
- Pilot projects: Companies can start with smaller projects and measure their success before making larger investments.
- Cooperations and research projects: Collaboration with universities, research institutions or technology partners makes it possible to share costs and exchange knowledge.
Shortage of skilled workers and lack of know-how
The shortage of qualified personnel is one of the biggest challenges in implementing AI and robotics projects. Companies need experts who possess both programming skills and a solid understanding of machine learning, robotics control systems, and data analysis. At the same time, interface skills are in demand, as integrating AI or robotics solutions into existing processes also requires an understanding of business operations and strategic planning.
If these skilled workers aren't found in time, development will progress only sluggishly. To counteract this, many companies are focusing on the further training of their existing workforce. New learning formats, certification programs, and online courses make it possible to impart relevant AI and automation knowledge to employees without them having to give up their jobs. Another option is to intensify collaborations with educational institutions or startups that have already developed expertise in these areas.
IT infrastructure and data availability
Modern AI and robotics systems rely on a reliable and high-performance IT infrastructure. Large volumes of data must be collected, transmitted, stored, and analyzed. In production environments, real-time processing is also crucial – delays can damage machines or products. If the company network is unstable or too slow, AI applications will only be usable to a limited extent.
Besides infrastructure, the quality and availability of data are crucial factors. AI models need to be trained with extensive datasets so they can recognize correlations and learn from them. However, standardized formats or sufficiently labeled datasets are often lacking. Furthermore, concerns about data protection, trade secrets, and compliance exist in many areas, particularly in the B2B sector. Companies are therefore challenged to develop concepts for effective data management, such as implementing data governance policies and ensuring the secure and transparent handling of data.
Ethical and legal aspects
AI systems and robots raise a number of ethical and legal questions. The central issue is responsibility: Who is liable if an AI-powered application makes incorrect predictions or a robot reacts incorrectly in a critical scenario? Added to this are questions of data protection and privacy. AI applications that analyze personal data must comply with strict data protection guidelines. Furthermore, concerns are growing in many industries that AI systems could exacerbate biases and discrimination if the data used is not sufficiently diverse.
Furthermore, there are ongoing discussions surrounding the military applications of AI and robotics. Companies developing dual-use technologies face accusations that their products could also be used for military purposes. Ethics must be firmly embedded in corporate strategy to prevent misuse. In everyday applications, such as service robots or AI-based assistance systems for the home, data protection and privacy are crucial aspects that should be considered as early as the product development stage.
Acceptance and trust of the employees
Despite the enthusiasm for new technologies, it's crucial not to forget that the introduction of AI and robotics in companies brings about significant changes for employees. There are often concerns that jobs could be lost or that employees will be pressured by constant monitoring. It is therefore essential to communicate early and transparently how the technology will be used and what benefits it will bring to all involved.
“The future lies in the collaboration between humans and machines – not in their displacement,” is a frequently quoted guiding principle. Employees should be involved in decision-making processes so they can identify with the innovations. Further training programs and courses help to reduce anxieties and build confidence in dealing with AI, robotics, and automation.
3. Voices from industry and research
There is a broad consensus within the industry that AI and robotics primarily serve to enhance human capabilities and make work safer and more efficient. Many experts believe that a complete replacement of human workers by intelligent machines is neither realistic nor desirable.
Dr. Susanne Bieller, Secretary General of the International Federation of Robotics (IFR), is frequently quoted as saying: "There will be no artificial robot intelligence in the foreseeable future that surpasses human intelligence in all areas." She emphasizes that robots, especially in combination with AI, cannot completely replace humans in their adaptability, flexibility, and creative problem-solving skills. Instead, she sees the "most meaningful applications for AI in robotics in environmental perception and in optimizing robot performance.".
Professor Dr. Jan Peters, head of research at a renowned AI research center, also sees great potential in industrial robotics, especially given that in the future the environment will no longer have to adapt to the robot, but rather the robot will have the ability to adapt itself to different production environments. "I am convinced that robots will find their way into millions of households as soon as they become affordable," is a vision he has repeatedly expressed in interviews.
Michael Mayer-Rosa, a representative of a technology company, highlights aspects such as safety and reliability, the complexity of data processing, and ethical and legal concerns as the biggest challenges. Similarly, Jens Kotlarski, managing director of a robotics company, emphasizes the importance of AI for the flexible design of robot deployment, especially for complex tasks or in scenarios with dynamic changes.
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4. Success stories from practice
A look at successful implementations shows the potential of AI, robotics and automation when companies manage to overcome technical, organizational and cultural hurdles.
- Walmart: The company is using AI to optimize its supply chain, shorten delivery times, and improve inventory levels. Furthermore, Walmart is deploying AI-powered robots for inventory management. These efficiency gains have a positive impact on the entire value chain.
- Brother International: Brother International uses AI for recruitment. An automated system identifies suitable candidates, schedules interviews, and answers standardized questions during the application process. This has significantly reduced the time required to fill a position.
- Siemens: The company uses AI for predictive maintenance in manufacturing. By analyzing machine data, potential failures can be identified early and addressed proactively. This reduces downtime and increases productivity. AI models are also used to optimize and control production processes, reducing energy consumption and increasing production speeds.
- BMW: A humanoid robot is being used for the first time in one of its plants to support employees with heavy physical tasks. BMW is also testing the use of cognitive robots that use AI to perceive their environment and perform more complex tasks.
- Sereact: A company dedicated to so-called "embodied AI." Here, visual zero-shot reasoning and voice instructions are combined, enabling robots to perform tasks for which they were not explicitly trained. This flexibility can offer enormous advantages, particularly for use in factory halls and warehouses, especially where processes change frequently.
5. Types of robots in automation
Robotics has developed rapidly in recent years. There are different types of robots, each designed for specialized requirements and possessing its own strengths:
- Collaborative robots (cobots): Cobots are designed to work directly alongside humans. They are equipped with sensor systems to prevent accidents and are relatively easy to program. Typical applications include assembly work, precision work, and quality assurance.
- Autonomous mobile robots (AMRs): AMRs navigate their environment without fixed guidelines and can plan routes independently. This makes them very popular in logistics, for example, to transport materials from one place to another or to independently pick orders in warehouses.
- Humanoid robots: These robots imitate human form and movements. Their applications range from care and support to demonstrations at trade fairs. They are generally more expensive and complex than cobots or AMRs, but could become particularly interesting in the future, especially in areas requiring human interaction and fine motor skills.
6. Sustainability and energy efficiency
One aspect that has become increasingly important in recent years is the question of sustainability. AI and robotics can make production more environmentally friendly and resource-efficient in many ways. The automatic optimization of production processes helps to reduce material waste, optimize maintenance intervals, and use energy more efficiently.
For example, robots can be programmed to operate only when needed, or to switch to an energy-saving mode during periods of lower demand. Intelligent route planning in supply chains can reduce CO₂ emissions. Furthermore, sensors and AI analytics facilitate the identification of weaknesses in the production process, allowing for more targeted resource allocation.
Companies that actively pursue energy-efficient automation typically benefit not only financially. As stringent environmental standards and CO₂ reduction targets increasingly become competitive factors, sustainable production methods also enhance a company's reputation and secure long-term market advantages.
7. Costs and ROI of AI, Robotics and Automation
Cost factors
The total costs for the introduction of AI and robotics systems can be made up of many components:
- Acquisition of the physical equipment (robot arms, sensors, hardware)
- Software development and implementation
- Licensing fees for AI tools and data processing platforms
- Maintenance and service contracts
- Training and further education for employees
Calculating the ROI
Companies often evaluate AI projects based on their return on investment. This means calculating when the investment will be recouped through cost savings or additional revenue, and what profits can be expected in the medium term. It's important to consider that AI, robotics, and automation solutions not only directly save time and money, but often also improve product quality, employee satisfaction, and customer loyalty.
Practical experience shows that investments in automated processes can often pay for themselves within a few months if they are well-planned and implemented. A classic example is Robotic Process Automation (RPA) in administration or customer service, where repetitive tasks are automated and thus completed much more cost-effectively.
8. Impact on the world of work and qualification requirements
Change in the world of work
The use of AI and robotics can, on the one hand, replace routine tasks and thus threaten jobs, but on the other hand, it also creates new professional fields, for example in AI development, data analysis, or the maintenance of complex automated systems. New opportunities also open up in traditional professions when AI-supported tools simplify everyday work and allow for a focus on more complex, creative tasks.
This results in a shift in skill profiles: Where purely manual skills were sufficient in the past, basic knowledge of data processing, automation, and AI applications is now required. At the same time, human-machine collaborations demand a certain level of technical understanding and a willingness to adapt to new workflows.
New qualification requirements
Many studies predict that a significant proportion of the workforce will require further training or retraining in the coming years to keep pace with changes. The ability to use and understand AI applications will play a particularly crucial role. Those who can design, maintain, or further develop complex automated processes will be in high demand in the future.
Large Language Models (LLMs), AI-powered language models that can mimic human communication almost perfectly, are currently receiving considerable attention. These models can be used for a wide variety of tasks, such as automatic text generation, answering customer inquiries, or managing a company's knowledge base. It is estimated that LLMs could take over a significant portion of office work in the future, thereby increasing productivity in many areas. However, it is crucial that employees learn to use these systems competently and to critically evaluate them.
The “Triangle of Automation”
Discussions about the future of work often refer to the concept of the "automation triangle." It represents a balance between:
- Hardware automation (robotics, machines)
- Software automation (e.g., RPA, AI algorithms)
- Human workforce (with creativity, social interaction and flexibility)
"The key to success lies in optimally combining the capabilities of machines and human talents." In this philosophy, humans and machines should complement each other: machines take over the repetitive, strenuous and dangerous jobs; humans concentrate on tasks that require judgment, empathy or creative problem-solving.
9. New business models: Robot-as-a-Service (RaaS)
An interesting development in the adoption of robotics in businesses is the emergence of service models. Similar to Software-as-a-Service (SaaS), companies can rent robots and related services such as maintenance and support for a limited time instead of purchasing them. This approach is known as Robot-as-a-Service (RaaS).
Robotics as a Service (RaaS) makes it easier for small and medium-sized enterprises (SMEs) to adopt automation technologies, as it eliminates high initial investments. The service provider typically assumes responsibility for the smooth operation of the robots and for regular updates. This reduces the risk of costly misinvestments and accelerates implementation. At the same time, RaaS is a business model that fosters continuous innovation, as manufacturers constantly work on improvements to remain competitive in the market.
10. Legal and ethical concerns
Legal challenges
In healthcare, but also in other sensitive areas, the issue of liability and approval of AI systems is being intensely debated. A key question is: How can continuously learning systems, whose behavior constantly evolves during operation, be certified? Traditional approval procedures are mostly static and only partially reflect the nature of self-learning algorithms. Future legal frameworks must therefore establish rules for how software updates and newly acquired skills are legally assessed.
Ethical aspects
Beyond the legal aspects, ethical questions are also pressing. The development of AI that can be used for military purposes raises ethical dilemmas. Companies face the challenge of ensuring that their technologies are not used for unethical purposes. Furthermore, it is essential to avoid so-called "bias" in the data so that algorithms can make fair decisions.
Privacy and data protection also play a major role. Smart devices in the home, such as robotic vacuum cleaners or digital voice assistants, continuously collect information about their environment. Users must be able to rely on the fact that this data is secure and will not be misused.
11. Future Trends in AI-Based Robotics
The further development of AI and robotics will become increasingly visible in more and more areas of life and work in the coming years. Several trends are emerging:
Adaptive learning and flexible automation
AI systems will increasingly be able to analyze their environment and spontaneously adapt their behavior. This makes robotics solutions more versatile and enables more efficient use in changing production environments.
Edge computing
To reduce latency and process data more securely, many companies are moving AI functions to local devices (edge devices). This allows robotic systems to react in real time without relying on an external cloud.
Lightweight construction and modular systems
Robots are becoming increasingly lighter, more modular, and easier to program. This lowers the barriers to entry for companies that want to automate.
Improved human-machine interaction
The interfaces between humans and robots are becoming more intuitive. Natural language processing and gesture recognition can lead to even smoother interaction. Furthermore, new development tools and programming environments allow for rapid adaptation to individual application scenarios.
Integrating AI into everyday life
Besides industrial applications, AI-supported robotics will increasingly appear in private households and public spaces. For example, delivery robots, cleaning robots, and digital companions for the elderly are conceivable areas of application that will continue to grow in importance in the future.
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12. Recommendations for companies
To best exploit the potential of AI, robotics and automation and to successfully overcome existing challenges, the following recommendations are offered:
Clear definition of goals
Companies should clearly define what they want to achieve with AI and robotics. Only those with clear goals and key performance indicators (KPIs) can assess whether a project is worthwhile and what steps are necessary.
Step-by-step implementation
It can be beneficial to start with smaller pilot projects to gain initial experience. This will help identify which technologies are particularly suitable for your specific environment. Successful pilot projects can then be scaled and expanded to other areas.
Investment in further education
The human factor remains central to automated processes. High acceptance and effective use of new technologies can only be achieved if employees receive timely and thorough training. This builds trust and improves results.
Collaboration with experts
Developing an AI or robotics project often requires an interdisciplinary team. Companies benefit from seeking partners – whether in the form of collaborations with startups, research institutes, or specialized service providers.
Consideration of ethical and legal aspects
When introducing new technologies, data protection, data security, and ethical principles must not be neglected. Early legal review and the involvement of relevant experts prevent problems and strengthen public trust.
Sustainability in focus
Advanced AI and automation solutions should always be considered from a sustainability perspective. Companies that pursue resource-efficient approaches strengthen their competitiveness and contribute to climate protection.
The path to intelligent production: Strategies for companies in the AI age
AI, robotics, and automation are no longer just futuristic concepts; they are already being used successfully in companies worldwide. They hold enormous potential for increasing productivity, reducing costs, and making working conditions safer and more attractive. At the same time, however, they are fraught with challenges: from security concerns and regulatory requirements to skills shortages and ethical and legal issues.
Nevertheless, numerous practical examples demonstrate the value of strategically planned deployment. Companies like Walmart, Brother International, and Siemens are showing how AI and robotics projects can optimize supply chains, accelerate recruitment processes, and make production processes more efficient. In the automotive industry, manufacturers like BMW are deploying the first humanoid or cognitive robots to relieve employees of physically demanding tasks.
Experts from industry and research confirm that it is worthwhile to promote human-machine collaboration rather than focusing solely on a fully automated future. For long-term success, a balanced approach is crucial, combining the capabilities of hardware, the possibilities of software automation, and the irreplaceable creativity, flexibility, and experience of humans.
Last but not least, issues such as data management, ethics, data protection, and sustainability are playing an increasingly important role in the development of modern AI and robotics systems. Only those who take responsibility for the responsible and safe use of these technologies will be successful in the long run – both economically and socially.
Overall, AI, robotics, and automation are experiencing strong growth and are opening up new opportunities for companies in almost every industry. However, it is crucial not to be driven solely by enthusiasm for the technology, but also to consider the organizational, legal, and human aspects. Only then can intelligent production become a reality and create long-term added value for all stakeholders.
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