Many companies see the potential for AI in SAP, but don’t know where to start. At aiLAB, we help you identify AI use cases for SAP, evaluate them according to business value and feasibility, and develop actionable next steps.
aiLAB – Discovery Call
Artificial Intelligence has long been a strategic priority in many SAP organizations. However, a clear evaluation framework is often lacking in practice. The focus is not on whether AI is relevant, but rather on where it makes sense to apply it in a specific context.
A structured AI use case assessment provides clarity in this area. Rather than starting with technologies or general trends, we look at your processes, pain points, data landscape, and goals within the SAP ecosystem. This approach avoids loose ideas and instead focuses on prioritized AI use cases backed by a solid foundation for decision-making.
In many companies, AI fails not because of the technology itself, but because of a lack of focus. There are initial ideas, isolated pilot projects, or strategic pressure, but no common framework for evaluation and prioritization.
Common issues:
This is particularly critical in the SAP context. Here, business relevance, data availability, governance, the system landscape, and feasibility must all align.
Nicht jeder KI-Anwendungsfall, der im SAP-Kontext interessant erscheint, ist auch sinnvoll. Relevant wird ein Use Case erst dann, wenn er ein konkretes Problem adressiert, zu den bestehenden Prozessen passt und sich realistisch umsetzen lässt. Deshalb bewerten wir SAP-nahe KI Use Cases anhand klarer Kriterien:
We help companies systematically identify, evaluate, and prioritize AI use cases within the SAP environment. Our focus is not on generating as many ideas as possible, but on identifying the right ones: those that are business-relevant, technically feasible, and organizationally viable.
At aiLAB, we bring together:
Our focus is on AI use cases that are grounded in real-world processes, are conceptually sound, remain technically feasible, and can be translated into a meaningful roadmap.
The right way to get started depends on how far your organization has already come with AI. Not every team needs a prototype right away. It often makes more sense to first clearly categorize, evaluate, and prioritize use cases. Our formats support a variety of starting points:
From initial exploration to prioritization: We help you identify and further develop AI use cases within the SAP context.
aiLAB – Discovery Call
REALTECH aiLAB provides a solid foundation for future decisions and transforms an abstract topic into a clearer framework for action.
Let’s work together to identify which AI use cases will have the greatest impact in your SAP environment.
aiLAB – Discovery Call
Typical applications include ticket classification, document processing, knowledge assistance, data quality checks, process evaluation, and support for testing and analysis. However, the most appropriate use cases depend heavily on your processes, data, and objectives.
It makes sense to evaluate ideas based on business value, feasibility, and organizational compatibility. What matters is not just the idea itself, but whether it can realistically be developed into a pilot or MVP.
Ideal for teams that are looking for guidance, want to better understand AI in the SAP context, and are just beginning to systematically identify potential areas of application.
A good AI use case requires more than just an interesting idea. It is essential to have a clearly defined process, usable data, demonstrable business value, and a realistic organizational and technical framework. In the SAP environment in particular, governance, the system landscape, and integration capabilities also play a key role.
An MVP or pilot is worthwhile when a use case has been clearly defined from a technical standpoint, relevant data is available, the benefits have been plausibly assessed, and there is a realistic framework for testing. Only with this groundwork can an interesting idea be turned into a meaningful first step toward implementation.
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