Digital Challenges for the Automotive Industry – An Overview

As an expert in digitalization within the automotive industry, I witness every day how companies strive to keep up with increasingly complex challenges and how difficult it often is to manage the digital transformation. The automotive industry is undergoing a fundamental shift — technologically, economically, and socially.

In this post, I would like to outline the key challenges faced by Original Equipment Manufacturers (OEMs) and, based on my experience, highlight the critical levers for a successful digital transformation. I enjoy finding solutions to these challenges and hereby begin a series of posts on the topic.

Overview:

1. Customers and Markets are changing

2. Global challenges

3. Systemarchitectures between legacy and digital future

4. Technology & Workforce as key components of digitalization

5. Conclusion and outlook

1. Customers and Markets are changing

Individual Customer Needs Increase Complexity

This trend has been evident for years: Lot-size-one – maximum individualization.

Traditional vehicle sales are increasingly evolving into offerings of flexible, customized mobility solutions. Customers now expect not only high-quality vehicles, but also personalized user experiences – both online and offline. This demand is leading to a significant increase in the number of product variants OEMs must offer. It directly impacts development cycles, manufacturing processes, and data management. Data management is the key term here – and the foundation for process innovation. Every SAP consultant knows what end users don’t want to hear: clean master data and the associated maintenance effort always pay off(!), because only clean data enables flexible, robust, and innovative processes to work effectively.

Electromobility as the New Standard

Certainly not news, but still highly relevant! Electrification continues to grow – not only due to political frameworks but also because of increasing environmental awareness among consumers. In Germany, around 200,000 EVs were newly registered in 2025 by May, representing an increase of over 40% compared to the (admittedly weak) previous year. OEMs must rethink their architectures, platforms, and infrastructure to make the transition to EVs efficient and scalable. The integration of over-the-air software updates, charging infrastructure, and energy management is increasingly becoming a key differentiator.

Autonomous Driving – A System Shift

Autonomous driving is more than just a technological gimmick. It transforms the vehicle into a rolling data center and the OEM into a provider of safety-critical software solutions. Sensor fusion, AI-based decision-making models, and regulatory certifications must be integrated in a highly dynamic environment – a task that goes far beyond traditional vehicle development. What has been underway in the U.S. for some time (e.g., Waymo) is just beginning in Germany with pilot projects such as KIRA or soon also the ID. Buzz AD (VW/MOIA). It remains an exciting area to watch.

2. Global Challenges

Global Crises, Wars, and Trade Tensions

The past few years have shown that geopolitical crises – such as the war in Ukraine, tensions between China and the U.S., or regional trade conflicts – have a massive impact on supply chains. In addition, protectionist measures such as tariffs or export restrictions further undermine planning security. OEMs must learn to build resilience into their global value networks through redundancy, diversification, and digital transparency. Integrated ERP system landscapes with easy access to real-time data make life easier for top decision-makers by enabling faster response times.

Supply Chains Must Be Digitally Connected and Robust

Digital ecosystems like Catena-X exemplify how transparency along the supply chain can be achieved through standardized data spaces and collaboration platforms. These solutions offer a promising approach – but the key lies in their practical integration into existing systems and processes. The journey from vision to productive implementation is still long and requires a deep understanding of the interconnection between IT and operational reality. This area holds immense potential for collaboration between OEMs and suppliers. However, differing platforms, data structures, and integration technologies across companies continue to hinder traceability and cooperation – especially in areas like root cause analysis.

Disruption from New Market Entrants

In addition to traditional risks, new players are entering the scene: software-driven start-ups and tech companies operate at high speed with data-centric business models and a holistic understanding of the customer. Building a car is no longer rocket science, and up-and-coming brands like Zeekr or SERES are putting long-established companies under pressure – particularly in China, one of the most important sales markets. OEMs must respond by reorganizing their internal innovation capabilities – through agile teams, digital platform strategies, and integration of external innovation sources.

3. Systemarchitectures between legacy and digital future

Legacy Systems as Innovation Barriers

Many OEMs still operate with IT landscapes that have evolved over decades. These systems are often stable, but also cumbersome. Their further development ties up significant resources and frequently conflicts with the agility required by modern digital business models. The real challenge is not only technological, but also cultural: legacy systems often symbolize outdated mindsets. However, replacing them consistently leads to several recurring issues, such as:

1. High integration with core business processes – posing a high risk of production downtime and cascading errors

2. Experienced users refusing to give up the old system

3. Lack of documentation and technical know-how for outdated programs

Not all past achievements are bad but software also is not the same it used to be 20 to 30 or even just 10 years ago. Often a radical shift, backed by top executives is required.

End-to-End Digital Integration

The value chain – from suppliers through production to end customers – requires a fully connected system landscape. Harmonizing and integrating these systems is not just an IT project, but a strategic necessity. Here, data quality, standardization, and real-time capability determine competitiveness. SAP has followed a centralized approach for the past 30 years, which led to numerous custom (Z) developments and complex systems. Its more recent strategy involves replacing this with an architecture built around a clean core and well-organized, connected external applications. This shift presents major challenges for many companies. When the “clean core strategy” is poorly implemented, vast potential is too often just wasted.

Cloud Technologies as Enablers

This is technically a subtopic of what was already mentioned above. But I can’t leave out the buzzword “cloud”, because:

Cloud infrastructures are key to scalability, innovation speed, and global data availability. They enable agile development environments, shorter time-to-market cycles, and new services. The challenge, however, lies in the transformation itself – moving to the cloud is not just a technology project; it also requires new governance models, partnerships, and security frameworks.

Cybersecurity as a Corporate Responsibility

With growing digitalization, the attack surface for cyber threats increases dramatically. Security strategies must therefore be proactive, systematic, and holistic. OEMs are responsible for protecting not only their IT systems but also their vehicles, production networks, and customer data. Cybersecurity is becoming a core responsibility of corporate leadership – and a crucial trust-building measure with partners and customers.

I know all too well how annoying this can be (mandatory trainings, phishing simulations, etc.), but the importance of digital data protection should not be underestimated. That said, cybersecurity is always first and foremost a people issue – social engineering remains one of the biggest risks to companies and their IT systems.

4. Technology & Workforce as key components of digitalization

Automation and Smart Manufacturing

The digitalization of production – from robotics and digital twins to AI-powered defect analysis – is a key driver for efficiency, quality, and sustainability. But once again: technology alone is not enough. Real value is only created when these systems are intelligently orchestrated, fed with real data, and strategically aligned.

The vision of “lights-out factories” may be an inspiring long-term goal, but the real success factor lies in the smart integration of humans, machines, and data. Production workers need new skills to interact safely and efficiently with digitalized systems. At the same time, IT teams must learn to understand physical production processes. Sometimes I feel more like a translator between these two plaines than a consultant. But it is crucial to speak the same language!

AR/VR as a Bridge Between Humans and Machines

Augmented Reality (AR) and Virtual Reality (VR) are gaining importance – whether for maintenance support, training new employees, or visualizing complex products during development. These technologies not only enhance efficiency but also improve employee satisfaction through intuitive operation and more understandable workflows. Sure, it’s no longer the hottest trend and certainly not a game changer like AI – but I still think it’s cool and worth mentioning.

AI as a Strategic Tool

Artificial Intelligence unleashes its full potential where large volumes of data exist – such as in quality control, process optimization, or predictive maintenance. The key to success lies in embedding AI initiatives within a clear strategic framework and developing a sound data strategy. Without trustworthy, clean, and accessible data, every AI initiative remains pure theory (as noted in Section 1 – clean data is absolutely essential!). Not only that – a clear use case is also necessary to unlock AI’s potential. Every software marketing expert throws the term “AI” around, but the expected productivity boost often fails to materialize because many people still don’t know how to make the best use of this fancy new tool.

Skilled Labor Shortage as a Structural Challenge

Digitalization (in the automotive industry) is currently not held back by technology, but by the lack of qualified professionals. Data scientists, cloud engineers, OT security specialists, legacy elimination engineers, digital synergy overlords, predictive analytics prophets, dark web intelligence ninjas, or digital downtime therapists – just to name a few – are in high demand, and the supply is far from sufficient.

OEMs must therefore actively invest in retraining, internal development, and partnerships with educational institutions. At the same time, they need to position themselves as attractive employers in the digital space – with modern work models, clear development paths, and a culture that embraces innovation.

Note: There is also a shortage of professionals to maintain legacy systems (see also Section 3), which makes change not just desirable but absolutely necessary.

Conclusion: Data, Mindset, Perseverance – and the Digital Shift

Digital transformation in the automotive industry is not a one-off IT project with a clear deadline, but a profound change that affects technology, processes, and mindsets alike.

Whether it’s customer expectations, new competitors, fragile supply chains, or outdated IT systems – the challenges are significant. But within these challenges also lie real opportunities: for greater flexibility, new business models, and improved customer experiences.

What’s needed are clean data, a clear focus, and the willingness to rethink established approaches – even when it gets uncomfortable. Digitalization is not an end in itself. It’s the tool we can use to reshape mobility. Step by step, with common sense and a healthy dose of curiosity.

Looking Ahead: More Insights Coming Soon

In the coming posts, I’ll dive deeper into selected aspects of digital transformation in the automotive industry. The focus will be on practical, real-world questions from the following areas of application (at least that’s my current plan – if I come across something more exciting, I’ll adjust accordingly. After all, I’m agile and flexible):

• Production Processes and Digital Manufacturing Systems: Analysis of real-world use cases for automation, connectivity, and optimization in production – with a focus on efficiency, scalability, and implementation within existing structures.

• Digitalization in Quality Management: Opportunities and limitations of data-driven quality: from integrating new technologies to cross-system defect analysis and AI-supported decision-making.

• System Architectures and Implementation Projects: Experience-based perspectives on the rollout of modern IT solutions, dealing with legacy systems, cloud concepts, and their practical execution.