Artificial Intelligence in Business: How AI Creates Real Added Value in Practice

Key Takeaways

• AI delivers its greatest value where it not only makes processes more efficient but also creates new business models, more accurate forecasts, and better customer experiences.
• Successful projects solve a specific problem, are based on stable data, are integrated into processes, and have clear goals and success criteria.
• In the railway sector, a predictive model for RailNetEurope was able to increase the accuracy of arrival time forecasts to over 95%.

After a company has taken its first steps toward artificial intelligence, a crucial question often arises: Where and how can AI be deployed to create real, measurable value?

Because AI is not a short-term trend, but a gamechanger. It unfolds its full potential not where it merely makes existing processes slightly more efficient, but where it opens up new perspectives—such as more accurate forecasts, new business models, or better customer experiences.

How Can You Identify Meaningful AI Projects?

In our experience, there are several criteria that successful AI initiatives share:

• They solve a specific, relevant problem—not just a theoretical one.
• They are based on a stable data foundation.
• They are closely embedded in both technical and operational processes.
• They have clearly defined goals and success criteria.
• They deliver value that goes beyond pure automation.

AI only unfolds its full potential when tailored to the specific business and technological context. This is when it makes a real difference—not just another tool running in the background.

In Which Industries Is AI Used?

Example: Mobility and Rail Transport

For RailNetEurope (RNE)—a consortium of European railway infrastructure managers—we developed an AI model that predicts precise arrival times for around 30,000 trains in over 21 countries.

Instead of estimates, we now have over 95% accuracy—powered by real-time data, historical analysis, and customized algorithms. A true boost for transparency and planning in international rail transport.

Prerequisites for Successful AI Processes

Such projects only succeed when three conditions are met:

• Internal expertise – Through in-house AI use cases and skills development.
• Strategic clarity – What the project is supposed to deliver.
• Collaborative implementation – Working as equals with the customer.

Especially in safety-critical areas such as mobility, it becomes clear that standard solutions quickly reach their limits. What is needed here are tailored concepts, transparency, and the trust of all stakeholders.

Strategically Deploying Artificial Intelligence in Business

Once the fundamentals are in place—knowledge, acceptance, initial experience—it’s time to take the next step: to deploy AI where it can drive real progress.

Those who think strategically and are willing to explore new approaches will get more out of AI than just efficiency gains. They will use it to sustainably develop and grow their business.