Once again, the integration of artificial intelligence into the automotive industry marks a turning point for the manufacturing world and Industry 4.0. As in all other sectors of the economy, AI promises enormous potential for greater efficiency, innovation and new business models. Of course, all this comes at a price and is not without challenges and risks. We will look at both the light and the shadows in relation to AI and the automotive industry, what this means for professionals, and finally present four future scenarios that will soon become reality or are already part of it.
Positive effects of AI integration
Many of you already know about the possibilities of AI, so we will keep this chapter rather short.
Increase in production efficiency
- Automation of processes: AI enables the automation of complex manufacturing processes, resulting in higher productivity and lower error rates.
- Predictive maintenance: Predictive maintenance can minimise downtime and extend the service life of machines.
Personalisation and new business models
- Individual vehicle configuration: AI makes it possible to offer customers tailor-made solutions, which increases customer satisfaction.
- Data-driven services: Development of new services such as connected mobility and personalised offers.
Supply chain optimisation
- Real-time monitoring: AI improves transparency in the supply chain by continuously analysing data.
- Efficient logistics: optimisation of routes and inventories using AI algorithms.
Improvement of product quality
- Quality control: AI-supported systems recognise errors in real time and enable immediate corrections.
- Design and development: AI supports engineers in simulations and data analysis to develop novel solutions. This includes the use of new materials or even the development of new composites.
Possible pitfalls and risks of AI implementation
AI and cars – what could possibly go wrong? Well, a lot, actually. And we don't even want to touch on the subject of autonomous driving, because here we are talking about manufacturing.
Data security and data protection are clearly a central problem. The increasing networking of production facilities and vehicles increases their vulnerability to cyber attacks. Ransomware sends its regards! Companies must make significant investments in security solutions and also comply with strict data protection laws such as the GDPR to protect sensitive data.
High implementation costs are another obstacle. The integration of AI systems can involve significant financial expenditure – and the return on investment is often uncertain. This is an economic risk, especially for smaller companies. Furthermore, the technological complexity of AI systems is a challenge. Integration or migration is always proven to involve risk. And there is a danger of becoming dependent on certain technology providers, which can limit flexibility and innovation. This also applies to MES systems (more on this later), where manufacturers often offer proprietary solutions that create a high level of dependency. If a provider discontinues further development or increases prices, companies could have difficulties finding alternatives without incurring high switching costs.
An often-overlooked but critical issue concerns quality assurance for AI-based decisions. AI systems can make decisions independently, but incorrect predictions can lead to production downtimes or quality problems. If AI systems are configured incorrectly or access faulty data, they can cause unpredictable errors in production. Therefore, professionals are needed to develop and regularly monitor the AI models to minimise such risks.
And last but not least: the risk of a shortage of skilled workers and existing skills gaps. The new technologies require professionals with specialised knowledge of AI and data analysis, but there is a shortage of qualified personnel in this field. Companies are called upon to invest in the advanced training of their employees in order to close skills gaps as far as possible – regardless of whether organisations have access to these experts or not.
The future of the automotive industry
Even beyond artificial intelligence, the automotive industry is in a state of constant change. New technologies are transforming existing processes or replacing them altogether. In the following, we will look at four areas that are currently in a state of upheaval or have already been transformed.
Software integration in (automotive) production
Integrating software into automobile production is a central component. The networking of machines and manufacturing systems results in flexible, automated production that is geared towards efficiency and adaptability.
Along with this, the requirements for professionals are changing significantly. In addition to traditional engineering knowledge, a deep understanding of digital technologies and software solutions is becoming increasingly important. Skills such as IT security, data analysis and a command of communication protocols (such as OPC UA or MQTT) are essential. In addition, engineers must increasingly have knowledge of automation technology and programming in order to control and optimise processes.
Particularly in demand are experts who can implement process automation and master the integration of automation software into existing production systems. Knowledge of programming languages used in industrial automation (e.g. Python, C++ or special PLC programming languages) is required here.
As a result of the ever-increasing digitisation and automation in automotive production, both the demands on technical knowledge and interdisciplinary skills are increasing. Professionals who combine these skills will play a central role in the new era of intelligent production.
Positions related to software integration
- Automation engineer
- Production engineer for digital systems
- Industry 4.0 software developer
- Mechatronics engineer:
- Production engineer
- IT system integrator
Cloud-based Manufacturing Execution Systems (MES)
When it comes to optimising processes in modern automotive production, a future-oriented manufacturing execution system from the cloud is another key. A cloud-based MES offers flexibility, scalability and real-time data analysis that conventional, locally installed systems cannot achieve. It enables companies to respond more quickly to changes, better manage capacity and reduce costs by reducing the need for extensive in-house hardware.
Handelsblatt emphasises that AI and robotics are laying the foundation for the ‘factory of the future’, in which processes run largely autonomously. This is a crucial aspect of cloud integration. In the future, MES systems could use AI to automatically adjust production plans, predict maintenance work and optimise manufacturing processes without human intervention. This is creating a closer link between cloud systems and AI, allowing for even greater efficiency and flexibility.
However, the integration of such systems also places new demands on professionals. Engineers must not only have the technical know-how to maintain and manage cloud-based systems, but also have knowledge in the field of data analysis. This data is essential to exploit the full potential of cloud-based MES solutions. In addition, they must be able to embed AI models in production environments to increase the level of automation.
In addition, professionals must have a firm grasp of IT security to ensure the secure transmission and processing of sensitive production data in the cloud. Cybersecurity and network management skills are therefore essential, as is a thorough understanding of the interfaces between different systems to ensure smooth operation.
Positions related to manufacturing execution systems
- MES-Engineer (specialising in the cloud)
- Cloud Solutions Architect
- IT Infrastructure Engineer
- Data Analyst for production systems
- Cybersecurity Specialist for production systems
- Cloud Integration Specialist
No-Code and Low-Code
No-code and low-code platforms are transforming automotive production by enabling professionals without in-depth programming knowledge to develop their own applications. These tools accelerate the implementation of automation processes and reduce dependence on specialised IT experts. They offer a significant advantage for Industry 4.0, as they allow engineers and production staff to quickly respond to operational challenges and develop solutions.
The ability to work with these platforms is increasing. Engineers need to understand the basics of no-code and low-code tools in order to carry out process automation and system customisation themselves without extensive IT support. At the same time, these platforms expand the digital skills of the workforce and lower the barrier to entry for innovative applications.
Although in-depth programming knowledge is not required, a basic understanding of logic, data structures, programme and process design remains essential. Professionals who have mastered these tools can increase effectiveness in networked production.
Positions related to no-code and low-code
- Process Automation Engineer
- No-code/low-code developer
- Production planner with a focus on process digitisation
- Expert in process automation
Taking an all-round approach to AI security
As already mentioned, the security of AI in automotive production is a central topic of Industry 4.0. With the increasing integration of AI into production processes and connected vehicles, the risk of cyber attacks and security vulnerabilities is significantly increasing. A comprehensive security strategy must therefore cover all aspects – from data processing to the machines themselves.
For professionals, this means that in addition to a technical understanding of AI, in-depth knowledge of IT security and data protection is also required. Engineers and IT specialists must ensure that AI systems are both protected from external attacks and operate transparently and traceably internally. Skills in encryption, secure data transmission and the implementation of security protocols are critical for this.
Positions around AI security
- AI Security Engineer
- Cybersecurity Specialist for AI
- IT security expert for autonomous systems
- AI risk analyst
- AI security system architect
Conclusion
The automotive industry is entering a new era characterised by AI and connectivity. Professionals in production must once again adapt to new technologies and digital tools, from no-code and low-code platforms to cloud-based MES systems. At the same time, AI security is of the utmost importance to minimise the risks of digital transformation. Those who combine these skills will play a key role in shaping the future of automotive production.
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