Artificial intelligence is a welcome addition when it comes to accelerating processes, reducing costs, and improving product quality. However, many pilot projects stall as soon as they leave the safe laboratory environment. Those who want to use AI sustainably need more than just good algorithms: they need structure. This is precisely where Enterprise Architecture Management comes in. In the Datenfabrik.NRW project, the companies CLAAS and Schmitz Cargobull tested this approach with the help of it's OWL.

What is Enterprise Architecture Management?

Enterprise Architecture Management, or EAM for short, is a model-based approach that integrates business objectives, business processes, IT systems, data, and infrastructure like gears in a machine. This creates a transparent blueprint for the targeted introduction, operation, and scaling of new technologies.

The benefits are tangible: EAM reveals optimization potential, aligns the IT architecture with the requirements of the business units, and provides reliable decision-making bases for every investment in digitalization and AI.

The four levels at a glance

• Strategic level: Vision, mission, and long-term goals form the guiding principles. Here, all business and IT initiatives are prioritized to ensure that every activity supports the company's success.

• Business architecture: Processes, roles, and skills are clearly described and harmonized. This prevents media breaks and creates uniform workflows before the first line of code is written.

• Information system architecture: Applications and data flows are structured. Uniform data models ensure that information is consistent, secure, and available at all times.

• Technology architecture: Hardware, networks, and cloud platforms provide a scalable, high-performance, and secure infrastructure. This is where the smoothness of AI models in shift operation is determined.

Practical insights from the data factory in North Rhine-Westphalia

“We had the data – but nobody knew exactly where it was located, who owned it, or whether it could be relied upon. Only with EAM and data governance – that is, a clear structure and organization – did we become able to…” “From the pile of data, a trustworthy foundation,” says Jan Auerbach, Head of Corporate Data at Schmitz Cargobull.

The project demonstrates how theory becomes practice. Clean data sources, clearly defined responsibilities, and reusable interfaces made it possible to quickly transfer initial AI models from the pilot line to series production. Specialized EAM tools like SAP LeanIX or Archi visualize all dependencies and show at a glance where adjustments are needed.

Your added value at a glance

EAM removes the complexity from AI projects. You receive a clear blueprint for your digital future factory, avoid isolated solutions, and protect investments. At the same time, teams gain security because data quality, governance, and scalability are defined from the outset.

The white paper "Data Factory.Insights Part 3" explains the four EAM levels in detail, provides eleven proven rules for getting started, and shows how the 51 use cases of Data Factory.NRW were gradually transitioned into series production. Download the publication free of charge and strengthen your foundation for AI in production.

Come to the focus group

The it's OWL focus group "Enterprise Architecture Management" discusses methods, tools, and best practices of Enterprise Architecture Management (EAM) in an industrial context. The focus group demonstrates how EAM promotes the scalability and reusability of digital solutions, thereby creating the foundation for a future-proof and adaptable architecture. Particular emphasis is placed on the integration of AI-supported applications into existing production and engineering processes.

The article Enterprise Architecture instead of isolated solutions: How companies integrate AI into everyday life first appeared on it's OWL .