## 5 Discussion  
The SemDaServ approach should be adapted in practice depending on the specific requirements of the SSSE project as well as available resources (time, budget, staff, external experts). SemDaServ is theoretically applicable to all kinds of SSSE projects. For practical use, adapting SemDaServ to the particular SSSE project requirements is necessary to justify the expenses linked to the realization of SemDaServ. Indeed, conceptualizing SemDaServ in practice as a guiding framework rather than a rigid system makes its application more efficient. The four most important factors for adapting SemDaServ in practice are as follows:Number of product and service components  
2 Number and heterogeneity of actors to be involved  
3 Requirements for the modeling depth  
4 Method of operation (model-based or document-based)  
For Factors 1 and 2, the guiding principle should be \"as much as necessary, as little as possible\", because these factors exponentially scale the effort related to SemDaServ's application. For example, assume that predictive maintenance of the aircraft engine's main ball bearings would be the most critical element of a power-by-the-hour smart service. Then it would make sense to focus the data specification on the physical component main ball bearings and the smart service component predictive maintenance. This makes the relevant part of the product structure and the service structure very small. The actors required to carry out the data specification process would be, in addition to the Core Team, a few domain experts for ball bearings, and data scientists with knowledge in the area of service life prediction. This reduction to the core elements makes the SemDaServ approach feasible in a lean way in this case. Similarly, the principle of \"as simple as possible, as detailed as necessary\" applies to Factor 3. For example, it is not necessary to formalize every detail of possible signal waveforms from sensors on the main bearings as part of the creation of the information and data model. It is much more important to specify that suitable sensors must be installed in the engine to monitor the condition of the main bearings. Factor 4 should be aligned to the usual approach within the company. While using a model-based approach has benefits such as traceability or automatic updates of linked elements, the document-based approach is more accessible to a larger set of stakeholders, as it usually does not require specialized knowledge regarding software tools. For example, for small projects, it may make more sense to perform the SemDaServ approach in a document-oriented manner using information and data models created in Microsoft Visio than to completely abandon the use of the SemDaServ approach if neither software licenses nor the know-how to use a model-oriented approach is available. Regarding the working hypotheses the following conclusions are drawn from the evaluation presented in Section 4: WH1 (The probability of AI development with quality of results being in line with smart service requirements can be increased by a domain knowledge-driven data specification approach.) was confirmed in the interviews with the experts 7, 8, 9 (cf. Table 4 for looking up the IDs) as well as in both workshops. WH1 was not discussed in the remaining expert interviews because these expert interviews had a different thematic focus. The interviews and workshops revealed that a data-driven approach is currently used in practice for the development of AI components. This means that all available data is checked for its suitability regarding the realization of smart service requirements. Domain knowledge is of elementary importance here, since domain experts can use their knowledge to identify relevant data and distinguish causality from spurious correlation. If the available data is too small in scope or too low in quality, physical components of the SSS must be adapted, the quality requirements for the AI components must be reduced or, in the worst case, the SSS development project must be aborted.