## 1 Introduction  
This paper uncovers synergetic opportunities for integrating bundles of several different AI specializations in various services with a special focus on conversational systems. For this purpose, we use an extensive literature review and two case studies. In the two case studies, special attention is given to the analysis of conversational capabilities. The case studies are discussed, and future research opportunities are extracted and listed in the conclusion section. The idea of using Artificial Intelligence (AI) for service is not new [1]; however, there is considerable overlapping and blur between AI implementation in services and in the industry. For coherent analysis of this paper, we find it useful to describe the following hierarchy of AI techniques, tools, methods, and implementations into three distinct levels: (1) General AI techniques, (2) domain specialization AI techniques, (3) application tailored AI solutions. Each of these categories will be briefly discussed next and illustrated in Figure 1.  
1. The fundamental AI techniques could serve almost any purpose, and any application specialty. Some examples of pure AI techniques are pattern recognition, data mining (DM), machine learning (ML), deep learning (DL), rule-based reasoning, fuzzy logic, expert systems, etc. 2. The domain specialization AI techniques, generate a specific domain specialization (for example, natural language processing (NLP)) mostly by adjusting and improving one or more of the pure AI techniques (e.g., NLP may use pattern recognition, machine learning or deep learning); these domain specializations may possibly integrate other non-AI techniques as well (for example, statistics and statistical inference). 3. The application-tailored bundles; are already solutions for specific needs based on use cases. Figure 1: Hierarchy of specialization in AI tools and techniques. In the AI toolbox, fundamental AI techniques are the prevalent familiar tools, and using any of them as an independent decision is mostly unrelated to the synergies we shall discuss. The domain specialization techniques, on the other hand, have a broad potential for synergies. The synergies related to domain specialization techniques stem from the integration between domain specialization techniques themselves. The synergies are also related to adding new fundamental technique/s to a domain specialization. AI domain specialization developed in many directions: natural language processing (NLP), case-based reasoning (CBR), speech and tone recognition, face and emotion recognition, gesture recognition, collaborative systems and autonomous systems--to name a few [2]. Thus, each direction brought with it a specialization and specialists that focus on their field of specialty without mastering other AI fields [3]. For example, it is extremely rare to find affection computing experts that also specialize in autonomous systems; however, there are some AI fields that are closer than others to certain other AI fields. To visualize these relationships, in the service context, we map them to clusters as shown in Figure 2.  
Figure 2 groups the AI domain specializations, level II in Figure 1, into clusters of major AI specializations and their relationships. In the context of service provision, the main identified AI clusters are:  
* Speech-related cluster--AI techniques that focus on speech analysis and generation. * Text analysis cluster--AI techniques that focus on analyzing different types of text. * Emotional recognition cluster--AI technique that focuses on emotions recognition and analysis. * Computer vision cluster--AI techniques that focus on various methods of analysis pictures, photos and videos. * Collaborative cluster--AI techniques that focus on collaboration between human operators and digital machines. * Awareness cluster--AI techniques that focus on various awareness capabilities such as self-awareness and context awareness. A remarkable phenomenon is the usage of Gesture recognition in three different clusters.