Every organization has them: systems that have been running for years—sometimes even decades. Systems that “work just fine,” but in the meantime are quietly becoming more expensive every year. License fees, external specialists, maintenance contracts, upgrades, and IT teams that are primarily focused on keeping existing environments running rather than on innovation.
The question isn’t really whether these systems cost too much. They almost always do. The real question is: was the alternative ever affordable enough to make a serious move? For many organizations, the answer was simple for a long time: not really. But now things are different; modern data platforms make it possible to replace expensive legacy environments. And migration has finally become feasible.
Take SAP BW as an example. It was once a powerful and cutting-edge platform, but today it’s often one of the most expensive components in the modern data landscape. For organizations that manage these environments, none of this comes as a surprise. What’s often less obvious is just how different the alternative has become.
Let’s break down the hidden costs of staying put. A medium-sized organization with an SAP BW environment typically faces significant annual expenses:
Microsoft Fabric is a modern, integrated data platform that brings together data engineering, analytics, and reporting in a single environment. For organizations still running on SAP BW, this can make a huge financial difference.
Fabric operates as a usage-based SaaS platform. For many organizations, this results in annual costs that are significantly lower than current SAP BW licenses. The savings often amount to tens of percent, depending on scale and usage.
Fabric integrates seamlessly with tools like Power BI. This allows existing Microsoft-skilled developers and analysts to get started independently more quickly. Organizations become less dependent on a small group of expensive SAP BW specialists, while business users gain more opportunities to build and customize reports themselves.
Fabric is cloud-native and serverless. No more hardware management, no complex upgrade cycles, and no more infrastructure projects to worry about.
New data sources can be connected in days instead of months. The closed structures of traditional SAP BW environments give way to transparent pipelines that teams can control themselves.
All things considered, a transition from SAP BW to Microsoft Fabric can easily save a medium-sized organization between €200,000 and €500,000 annually. Of course, this depends on the size and complexity of the environment.
And yet, many organizations have not taken that step yet.
Because migration has traditionally been complicated, time-consuming, and risky. A classic SAP BW migration often involves manually analyzing and rebuilding thousands of objects: InfoObjects, queries, transformations, data flows, and custom logic. Projects like these take years, require large teams, and carry significant risks. That’s exactly where the paradox lay for years. The cost savings were clear, but the migration itself felt almost as expensive as staying put.
Instead of manually migrating object by object, the solution uses AI agents that automatically analyze, interpret, and reconstruct legacy environments on modern platforms such as Microsoft Fabric. The approach consists of five phases:
1. Analysis
2. Map
3. Reconstruct
4. Validate
5. Deploy.
Human oversight remains in place at every critical step, but the heavy and repetitive work is largely automated. As a result, the business case changes fundamentally:
And more importantly: the structural cost savings remain intact.
For many organizations, this means that modernization finally becomes financially and operationally feasible.
SAP BW is the most pressing example, partly due to its support expiring in 2027. But the same challenge exists in many other legacy environments. Think of older data warehouses, reporting platforms, custom solutions, or systems that only a few people within the organization truly understand. The AI agents behind the AI Migration Bridge can be trained on a wide range of legacy platforms. The principle remains the same: AI makes migrations feasible in a way that was previously simply too expensive or too risky. Perhaps that is the biggest change of all. Not only can organizations modernize from a technical standpoint, but they can finally say “yes” again to projects that were put on hold for years because the business case never added up.