Getting Started with AI in the DACH Region

SAP migrations, API integrations, DevOps transformations—many companies in the DACH region are actively modernizing their systems. And now, a new topic appears on the agenda: artificial intelligence.

All of a sudden, it’s: “We need to implement AI. Fast. Now.” Already, 67% of Germans use GenAI tools like ChatGPT (43%), Microsoft Copilot (39%), or Gemini (28%) at least occasionally. This creates significant pressure for companies to act—often greater than the actual technical challenges.

Yet the line between meaningful use and following a mere trend is often hard to draw. With professional AI consulting services, companies can implement smart tools in a targeted way.

Digital transformation in the DACH region: progress, gaps, and realities

According to the study “Legacy Modernization 2024” (CIO/CSO/Computerwoche & Thinkwise), 72% of German companies have ranked the update of business-critical systems as a high priority. However, the digitalization of many organizations in the DACH region is not yet complete. According to a Bitkom survey of 603 companies in Germany, 53% still face challenges in this area (as of 2025; in 2022, it was only 34%).

Processes are gradually being stabilized, systems consolidated, and employees trained on new tools. Yet Germany ranks only 14th out of 27 EU countries in the latest Bitkom DESI index. Expectations are rising, but the gap between ambition and reality remains significant. Here are the most pressing challenges:

• There is no centralized data warehouse consolidating essential information;
• The IT architecture is complex and heterogeneous;
• Employees are tired of constant transformations;
• AI specialists often struggle to integrate their solutions into existing systems.

Under these conditions, there is a risk that AI initiatives can be launched before fundamental structures are in place. In many cases, data quality, clear processes, and standardized workflows are still lacking.

The result is significant effort with limited impact.

Why an unstructured approach leads to a dead end

Artificial intelligence can’t just be ‘added on’ like any other tool. To use it meaningfully, companies need a different approach to digitalization—one that moves away from isolated actions and toward system-oriented, data-driven development of existing structures.

This does not mean that everything needs to be replaced at once or that the entire IT landscape must be redesigned. Instead, organizations should extend their existing structures in a way that allows intelligent software to deliver real value.

Even a few carefully implemented AI modules can produce considerable effects:

• They can be implemented in a much shorter time;
• Outcomes become visible and measurable quickly;
• The workload for business units and IT teams remains low;
• Companies can gradually test, adapt, and expand their AI strategies;
• Day-to-day operations are minimally affected.

In this way, AI potential is not forced but systematically unlocked—step by step and closely aligned with actual business needs.

What delivers the greatest impact in the DACH region

Above all, these are modular AI measures. They deliver truly convincing results. For example, companies could consider projects such as:

• Implementing a predictive maintenance module alongside SAP EAM. This allows downtime to be predicted without replacing core systems. Companies using such modules report 30–50% lower operating costs over five years.
• Leveraging AI through existing data warehouses. Existing data is used, eliminating the need to build new storage infrastructure.
• Introducing intelligent assistants into legacy systems. This can be done without a complete UI/UX overhaul. Employees receive suggestions or automated processes directly within the familiar interface.

These approaches minimize risk and deliver tangible value quickly. They also demonstrate that AI can work pragmatically, even in highly heterogeneous legacy environments.

Our concise guide shows practical ways to implement AI in the DACH region—including best practices, tips, and concrete module examples. Download now: https://form.andersenlab.com/ai-legacy-modernization

Successful AI implementation: Andersen’s approach

At Andersen, we believe that AI only works when it is embedded in existing systems, rather than remaining an abstract concept in presentations.

Our experts extend clients’ existing structures in a targeted way. We focus on areas where problems exist. Day-to-day operations remain stable while business units gradually benefit from intelligent tools.

In the DACH region, we have implemented solutions such as:

• AI modules for existing MES environments;
• NLP interfaces to enhance SAP systems;
• Predictive models for companies moving away from Excel-based workflows.

Thanks to our smart approach, we have cut modernization times for our clients by 40–50%, minimized downtime, and increased the efficiency of existing processes by more than 40%.

Of course, before any implementation, we review the data, processes, and system architecture of our clients. This helps us carry out AI initiatives without additional burdens.

Conclusion

Companies that have modernized or are currently transforming their IT systems need to decide how to use AI effectively. We recommend a gradual approach that complements existing systems. Modular AI solutions can improve processes, minimize risks, and deliver tangible results in a short time.