## 6 Discussion  
So-far, our discussion was generic, and we did not deal with specific implementations by a specific user, or a specific company. In this section we try to portray the approach that can utilize the information presented thus far, for the benefit of implementers. Specific implementation is always dependent upon the use case, product requirement, and market demand; it is therefore, that the requirement elicitation must be done so that the needs of the systems would be clear, prior to the decision on the AI framework to be developed; it is therefore assumed that at least a preliminary use-case analysis is already done, and the functional requirements of the system are already defined. From this point, we propose a sequential approach for generating an AI technology integration plan; this approach is illustrated and summarized in Figure 4.  
Figure 3: Capabilities evolution of the major AI specializations in conversational support service systems.  
The main considerations of this approach are: (1) the realization that different services may have vastly different characteristics and functional requirements. (2) the realization that performing a service function is the main goal of AI deployment in service provision. (3) therefore, relating AI technology to a service function is a key for assessing its importance. (4) however, immature AI technology, or the unavailability of a required AI technology are serious barriers that hamper this AI integration in service provision.  
Thus, we now describe the stages of generating an AI integration plan, after getting the service's main functions and transactions from a preliminary use-case analysis, and some non-functional requirements (mainly related to the interface).  
Figure 4: Proposed process for generating AI technology integration plan.  
The first stage is to evaluate and prioritize the importance of elements from a preliminary use-case analysis (both functional and non-functional) and generate an importance hierarchy (prioritization list) of these requirements.
2. The second stage is to derive AI requirements from the prioritization list (of functional and non-functional elements); this is to define the major outcomes required from the AI technology capabilities.
3. The third stage is to use AI capability evolution (Figure 3) to elicit required AI technologies, or combination of AI technologies, related to relevant functional or non-functional elements. In some cases, there is more than one way to achieve the same capability--For example, given available training data, one can build a single deep learning architecture for emotion recognition based on raw text and speech data, without building the intermediate modules illustrated in Figure 3. At this stage we show all alternatives to be chosen in the next stage.
4. The fourth stage is to determine the content of AI technologies in the implementation (based on contribution cost tradeoff. Including: priority, maturity and availability of each AI technology). At this stage, at most, only one AI alternative is chosen (e.g., deep learning vs. machine learning).
5. The fifth stage is to generate a conceptual rough-cut plan for implementing AI technology integration (possibly a conceptual Gantt chart).  
Figure 4 describes and summarizes the proposed process for generating an AI technology integration plan; however, it would take several case studies to test this approach and come up with insights as to its strengths and weaknesses.