Artificial
intelligence

Artificial Intelligence (AI) refers to systems that can perform tasks that normally require human intelligence – such as understanding language, recognizing images, or making decisions.

These systems rely on algorithms, training data, and models that enable them to identify patterns and learn from experience – for example through machine learning or deep learning.

Artificial intelligence enables companies to design processes that are smarter, faster, and more resource-efficient. It automates routine tasks like document processing or responding to inquiries, supports data-driven decision-making, and detects patterns that humans often miss.

As a result, AI helps lower costs, improve quality, and create new services.

Artificial intelligence can be applied across nearly all areas of a company – wherever decisions need to be made based on data, processes are to be automated, or content is to be analyzed.

Typical use cases include:

• Customer Service
Intelligent chatbots, self-service systems, or GPT-based knowledge assistants for faster response times and reduced costs.

• Marketing & Sales
Target group analysis, personalized content, campaign optimization, and automated lead scoring.

• Production & Maintenance
Predictive maintenance, quality control via image recognition, and optimization of machine parameters.

• IT & Workflows
Legacy code analysis, intelligent automation (e.g. via LLMs), anomaly detection, and workflow acceleration.

• Logistics & Supply Chain
Demand forecasting, intelligent route planning, real-time inventory optimization.

→ In short: AI is useful anywhere data, repetition, and decisions come together.

Many companies begin by experimenting with generative AI tools, only to realize: without a clear strategy, relevant data, and proper integration into their business processes, it often stays a pilot project. That’s where our AI consulting comes in. We apply a structured approach that connects business model, use cases, and technology – turning an AI experiment into a real business case.

An innovative data platform is the technical and strategic foundation of any successful AI initiative. It ensures that data from various sources is collected centrally, structured, made accessible, and processed securely. For AI, this means: models can only be meaningfully trained, validated, and used if the underlying data is consistent, up to date, and traceable.

We help companies build exactly that foundation – from strategic planning to architecture decisions to technical implementation.

Implementing AI usually starts by identifying which processes or decisions could benefit from automation or data-driven support. This also includes checking the availability and quality of the necessary data. Based on that, suitable use cases can be selected and implemented step by step – from concept development to prototypes and finally full integration into existing systems. An experienced AI service provider can support this process methodically and help overcome typical challenges early on.

Artificial intelligence offers opportunities but also comes with risks – for example regarding data protection, transparency, biases in training data, or incorrect decisions made by automated systems. Legal questions about liability or the use of generative models also remain partly unresolved. That’s why it’s important to design AI systems in a transparent, explainable way, to monitor them regularly, and to operate them within clearly defined boundaries.

Automation means executing clearly defined processes or rules using technology – often repetitive tasks with little variation. Artificial intelligence, by contrast, can learn from data, recognize patterns, and respond to new situations. While automation runs “by the book,” AI can also handle uncertainty – for example through natural language processing, forecasting, or decision support. Both approaches can be combined – for instance, when AI controls or optimizes automated processes.

Using generative AI (e.g. ChatGPT or image generators) requires clear rules and technical safeguards. Most importantly: handling sensitive data responsibly, ensuring transparency about AI-generated content, and protecting against misleading or incorrect outputs (“hallucinations”). Legal aspects like copyright, data privacy, and internal governance should also be clarified in advance. A secure environment – such as a dedicated AI platform – can help minimize risks and manage usage in a targeted way.