Artificial Intelligence
The question is not: How do I use AI? But: What problem do I want to solve?
Many companies start with the technology.
That's the wrong starting point.
Anyone wanting to use AI must first understand what business problem actually needs to be solved. Only then can you assess whether AI is even the right answer.
Often, it isn't.
AI is a tool. Not an end in itself. And certainly not a strategy.
AI is not just AI
In companies, we encounter different forms of AI – with completely different use cases.
Classical Machine Learning Models
Algorithms trained on data to:
- Recognize patterns
- Make predictions
- Support decisions
These systems have existed for many years.
They are precise, specialized, and clearly defined.
Generative AI
Systems that can create content, interpret, and respond flexibly:
- Understand and formulate texts
- Structure information
- Combine contexts in new ways
In process automation, generative AI is often a powerful tool –
but rarely the foundation.
Cognitive AI
Systems that understand complex relationships, interpret, and support decisions.
For example in:
- Document processing
- Decision support
- Structured knowledge management
This is less about creativity –
and more about structural understanding.
The Crucial Point
In many mid-sized companies, the greatest potential lies not in AI.
But in:
- cleanly defined processes
- clear architecture
- classical, deterministic automation
Many workflows can be mapped:
programmatically – without requiring an AI model.
AI in process automation is often the cherry on top of the cake.
Not the frosting. And certainly not the foundation.
When AI Actually Makes Sense
AI delivers value where clearly defined problems can be solved with the right tools.
Data volumes are too large for manual analysis
Patterns cannot be described deterministically
Interpretations become necessary
Forecasts are strategically relevant
Then deployed:
Deliberately and consciously deployed – not as an end in itself.
The Approach
Technology is not deployed because it's available. But because it meaningfully solves the problem.
Understanding the business problem
Reviewing process architecture
Clarifying what can be solved deterministically
Only then deciding if AI adds value
No AI hype.
No tool demonstrations.
But structured decision-making.
My Experience with AI Systems
Over the past years, I have practically implemented various AI approaches – always focused on real value rather than technical gimmicks.
Machine Learning
Classical ML models for forecasting and pattern recognition in business processes
Generative AI
Integration into automation processes for text processing and content
Cognitive Systems
EMMA for intelligent process automation
Hybrid Solutions
Combination of AI and deterministic process logic for maximum stability
What matters is not the technology itself –
but its embedding in a sustainable system architecture.
The Goal
Not "more AI".
But better decisions.
Not maximum automation.
But minimum complexity.
Not hype.
But clarity.
Artificial Intelligence for SMEs
Consciously deployed. Architecturally embedded. Business-minded.
Get in touch