AI: Hype or game changer?

What is Artificial Intelligence – or, put differently: how does it work? 

That’s a fascinating question that can be approached from different perspectives. Broadly speaking, AI encompasses a wide range of methodologies — from symbolic approaches that rely on clearly defined rules and logic to subsymbolic methods such as deep learning. The latter, which includes large language models, is based on artificial neural networks. These networks process vast amounts of data and learn patterns and relationships in the process. The training data — often referred to as a corpus — is compressed within these networks, ultimately producing probabilities that determine which response best fits a given input. As a logician, I find it particularly interesting that these models essentially represent inductive logic systems: their “hypotheses” about the world are stored in the weighted connections between neurons. 

Is AI a true game changer – or just hype? 

I’m convinced that AI will transform our world as profoundly as the internet once did. Of course, there have been exaggerated expectations in the past — I clearly remember the last AI hype around seven years ago, which didn’t live up to all its promises. What we’re seeing now, however, is a genuine breakthrough. The technology is mature enough to deliver real value in practice — even if we’re currently in the phase where prototypes need to be turned into production-ready solutions. I like to call this phase “the hard work of execution.” 

How and where can AI specifically support IT transformations? 

The major advantage of AI lies in its ability to bridge the gap between human language and machine-readable logic. For a long time, this gap was a major obstacle — natural language is flexible, context-dependent, and difficult to formalize. This is exactly where our applications come into play. AI can serve as an interface between humans and machines, automating complex tasks where language understanding is essential. 

Can you give a concrete example?

Absolutely. One example is an AI agent that discusses a transformation scenario with a customer and automatically selects and configures the appropriate content. This saves an enormous amount of time. The prerequisite is that the content is available in a machine-readable language — in our case, DCS Compose, our own programming language for DCS Migrate content, which can also be understood by AI systems.

Another example is our intelligent assistants within the Natuvion Data Conversion Suite. They combine information from product and project data and help resolve typical challenges in day-to-day project work quickly and reliably.

What’s important is that we don’t see AI in isolation, but as a complement to proven methods. Our goal is to largely automate the core processes of project delivery — and AI is a key enabler for that. 

You mentioned Natuvion’s AI strategy – can you tell us more about it? 

With pleasure. Our strategy is built on four pillars:

1. Languages 

   This includes both natural and artificial languages. We use natural languages such as German and English, as well as our own programming language, DCS Compose, as interfaces for human–machine interaction.

2. Data 

   Every transformation project generates massive amounts of metadata — for example, mappings, runtimes, and configurations. This data is a strategic asset and forms the foundation of our AI applications.

3. Automation 

   Automation is the key to delivering projects faster, more securely, and more efficiently.

4. AI 

   AI primarily handles the interpretation of natural language and helps automate complex tasks.

In short: with DCS Compose, AI, and the Natuvion Cloud Portal, we have the tools to reduce costs, cycle times, and complexity — while significantly increasing quality. 

What are the medium- and long-term goals of using AI in the Natuvion Data Conversion Suite – and what does this mean for customers? 

In the medium term, our goal is to largely automate the core processes of project execution — including configuration, documentation, support, and content selection. In the long term, we aim to industrialize transformation: moving away from one-off, highly individualized projects toward reusable, high-quality workflows supported by AI. For our customers, this means faster projects, lower costs, higher quality, and greater transparency. 

Which concrete features in the Natuvion DCS currently benefit most from AI – or will benefit the most in the future? 

We are currently focusing on developing support agents that provide guidance in every project phase and selectively access the content used in the respective project.  At the same time, we are working on an application that uses AI to generate mapping suggestions. Our goal is to support both our project teams and customers with intelligent assistant systems — from initial analysis through post-go-live support. In the future, areas such as DCS Protect and DCS Retire will also benefit significantly from AI, as many repetitive tasks can be automated. The vision is for AI to provide support throughout every phase of a project — from the initial analysis to post-project support. 

How will AI change the role of project teams in transformations in the long run? 

The role of project teams will change significantly. AI will take over many repetitive and technical tasks, allowing teams to focus more on strategic decision-making, quality assurance, and customer communication. This doesn’t make the work less important — quite the opposite. It becomes more demanding, more strategic, and more creative. 

What new skills do project teams need to work effectively with AI in the Natuvion DCS? 

In addition to a basic understanding of AI and automation, teams primarily need the ability to interact with intelligent systems — for example, communicating with assistants via natural language or evaluating AI-generated suggestions. Knowledge of our programming language DCS Compose is also helpful, as it forms the bridge between humans and machines. 

Do customers specifically ask for AI in their projects? 

Yes, definitely. Many customers are curious and see AI as an opportunity to deliver projects faster and more efficiently. They are particularly interested in automating complex tasks such as content selection and error analysis. 

Are there also customers who are skeptical about AI? If so, why? 

Absolutely. Some customers have concerns about control, transparency, or security. That’s completely understandable — and exactly why we place such a strong emphasis on governance, versioning, and human oversight. AI is meant to support people, not replace them. 

Many people perceive AI as a kind of threat. How do you see this? 

I can understand that perception. AI changes the way we work and challenges familiar processes. But I don’t see it as a threat — I see it as a tool. A tool that helps us make better decisions, work faster, and free up time for what truly matters. What’s crucial is that we use AI responsibly — with clear rules and human control.