From AI hype to implementation reality in the mid-sized industrial sector
No AI without data – digital infrastructure is the key to success
This article has been translated for your convenience using machine translation. Reasonable efforts have been made to provide an accurate translation. The official text is the German version of this content.
Artificial intelligence remains the trending topic, and enthusiasm continues to surge. This was once again evident at the Hannover Messe in early April. Not a single exhibition hall went without at least one booth focused on AI.
And the trend isn’t stopping at the industrial mid-sized sector either. A recent study by VDMA and Strategy& shows that, for example, the use of generative artificial intelligence could increase profit margins in mechanical and plant engineering by up to 10.7 percentage points. That would mean an additional €28 billion in profit for the industry. Companies facing concerns about productivity and profitability due to skilled labor shortages should urgently explore the potential of AI for their business.
In recent weeks, we’ve demonstrated the opportunities AI applications can unlock for mid-sized companies – from predictive maintenance to intelligent process optimization. The possibilities are vast. Many companies are just starting their AI journey, but want to get going soon.
This raises a crucial question: How can companies make a meaningful start with AI?
The answer lies in an often underestimated area – digital infrastructure.
1. Data is the fuel for AI – But not all data will get you to your destination
In recent years, the view has become widely accepted that data is the most important resource of the 21st century – the new oil. With the growing demand for and use of AI solutions, it is becoming increasingly clear why. AI systems are only as good as the data they are built on. Regardless of which business processes you want to improve – whether it's pattern recognition, forecasting, or decision-making – AI needs a large volume of high-quality, structured data.
Even AI agents that independently carry out tasks, make decisions, or automate entire process chains need a sound and clean data foundation. The more structured and contextualized the underlying information, the more effectively these agents can operate – for example, in customer service, quote generation, or supply chain optimization. The value lies not only in faster workflows or better results but also in significant cost savings. Intelligent agents can reduce manual workloads, take over repetitive tasks, and free up valuable resources – provided the data foundation is in place.
And this is exactly where the challenge often lies:
In practice, it's not just about having as much data as possible. The volume of data is no longer the main issue for many companies. But that data must also be:
- Accessible,
- Standardized and processed,
- Usable across systems.
To sum it up: It’s not the quantity of data that drives success, but its quality.
Without a clean foundation, even the most ambitious AI approach will fall flat.
2. Reality in the mid-sized sector: High potential, lacking structure
Especially in the industrial mid-market, the starting point is often complex: machine parks from various generations, legacy IT landscapes, data silos, and manual processes hinder the development of a clean data foundation.
Typical challenges include:
- No centralized data strategy or one that is not aligned with the overall business strategy;
- Lack of connectivity between systems in use;
- Incompatible formats and data structures;
- Limited access to relevant information.
The result: Although there is plenty of data, it often goes unused – or worse, it is used but leads to misinterpretation and inefficiencies.
3. Digital infrastructure: The backbone of smart applications
Before AI can be implemented, the digital infrastructure must be in place. It forms the backbone of any data-driven application – from basic visualization to fully automated decision-making.
A reliable digital infrastructure means:
- IT and OT systems are interconnected;
- Data is continuously collected and stored;
- A central, scalable data platform is available;
- Interfaces ensure smooth data flow.
It’s not just about technology. Organizational prerequisites – such as clearly defined responsibilities, data governance, and a comprehensive digital strategy – also play a central role.
Conclusion: Those who want AI must start with infrastructure
The enthusiasm for AI is well founded – it can provide real competitive advantages. But without solid digital foundations, the potential will remain untapped.
Anyone thinking about AI today should first think about data quality, system integration, and infrastructure.
In the coming weeks, we will focus on exactly this essential foundation:
How can industrial mid-sized companies build a digital infrastructure that makes them future-proof – and ready for AI?