Structured introduction of GenAI in the SAP environment

The key is a structured, business-driven approach that maximizes acceptance and impact instead of ending up in isolated technology experiments. The following steps will help you succeed.

Step 1 - Put the problem before the technology: The most common mistake: companies get excited about GenAI and only then look for a field of application. Successful initiatives start with clearly defined problems or inefficiencies. Interdisciplinary workshops in which specialist departments, technology experts and users analyze processes have proven their worth. Further information is provided by process data from SAP systems, service desk tickets and feedback from specialist departments.

Step 2 - Check GenAI potential: Can GenAI really make a contribution here - for example by creating text, summarizing, classifying or analyzing unstructured information or images? An initial rough check filters ideas according to expected benefit, effort, strategic fit and data availability. Unrealistic or less relevant projects are thus weeded out at an early stage.

Step 3 - Specify and prioritize the use case: The idea becomes a concrete use case. A use
Case canvas helps to clarify key questions: What problem is being solved? Who will use the solution? What does it look like - for example as a co-pilot, chatbot or document assistant? What data is needed and where is it located? We also examine how success will be measured. At the same time, a technical, organizational and ethical feasibility check is carried out. The benefits, costs and risks are then prioritized.

Step 4 - Proof of concept: Before committing large budgets, a proof of concept is recommended. In just a few weeks, a minimal setup is used to test whether GenAI can solve the core problem. The focus is on core functionality and prompt engineering. The aim is not just a technical test, but to prove that the IT landscape, processes and organization are ready. The result is a demo with reliable findings on acceptance, limits and data requirements - as a basis for the go/no-go decision.

Step 5 - Development of the first productive version: Only now does the development of the first operational version begin. It should be agile and focus on four aspects: Firstly, integration into existing SAP and non-SAP systems. Secondly, robust architecture including AI operating system (monitoring, logging, versioning). Thirdly, user experience that is geared towards users' day-to-day work. Fourthly: security and governance, such as protection against prompt injection, data protection and clear responsibilities.

Step 6 - Testing with pilot groups and rollout: What is needed? Output validation (correctness, style, bias), robustness against unusual inputs, security (data protection, authorizations, attack vectors) as well as performance and costs. Feedback loops with pilot groups - for example in UAT workshops in the SAP context - are mandatory. Based on this experience, a gradual rollout is carried out, starting with a limited user group. Professional change management, clear communication, targeted training and adapted processes are crucial to success.

Step 7 - continuous optimization: GenAI solutions are not static products, but living systems. New models, changing framework conditions and feedback from the specialist departments require continuous further development: better prompts, extended functions, readjusted governance. Ongoing monitoring of quality, costs and user satisfaction creates the necessary basis.

Conclusion

If you want to use GenAI successfully in the SAP ecosystem, you need a clear, iterative process. Companies that consistently bring together business departments, IT and data experts and understand GenAI as a user-centered learning process significantly increase the chances of measurable business added value. This is how GenAI is transformed from a hype topic into a tangible productive tool.

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