# On the Track to Application Architectures in Public Transport Service Companies
## 1 Introduction
Other than previous papers, that do an in-depth analysis of current challenges of incorporating AI-capabilities into the software engineering process from Microsoft in large scale [8], smaller organizations, which work in a specific business domain such as the transportation service sector, face similar challenges. However, they are most probably starting from a lower maturity level, both from the perspective of the organization, as well as from the perspective of the maturity level of their AI-based software components. Our paper collects cases from three different enterprises, in order to develop a common understanding of the necessary changes to implement AI-based application architectures in a specific business domain such as the public service transportation sector. For this purpose, we start with some general definitions based on the technical reports of the European Commission before we dive into some theoretical aspects of modelling the structural as well as behavioral aspects of AI-related software engineering. Based on these insights, we present different use cases of the different organizations, which all can be placed on a proposed AI landscape for the public transportation business domain. Current Frameworks and Maturity Models Which Could Be Used to Build Up an AI Landscape in Organizations  
The recent advancements in AI research in general and machine learning in particular have triggered broad interest also to global economies, to entire business domains and to governments and politicians. In the global context of Industry 4.0, AI is part of the EUs Digital Single Market Strategy and in the public transport sector, AI is a main topic in the UIC (International Union of Railways). However, as stated by a recent technology report of the EU, there is up to now no single, commonly agreed definition of AI but a rather huge variety of different definitions that emerged from early 1955 until today [9]. This report is focusing on an operational definition of AI where they are listing different AI core domains (CD) representing fundamental goals of AI as well as transversal domains (TD) referring to common issues of integrating AI core domains across disciplines and different knowledge areas. Both, core and transversal domains are listed in Table 1 with their appropriate sub-domains. Based on these domains and subdomains, they further collected different keywords which are relevant for the domains (e.g., case based reasoning, causal inference, expert systems etc. to name a few in the domain of reasoning) which can be used to build an entire AI taxonomy that is transparently mapped to the extensive list of definitions of AI between 1955 and 2019. In a second report from AI Watch, they list the AI-Categories with corresponding technologies as shown in Table 2. Furthermore, they define nine different Technology Readiness Levels (TRL, Table 3) with their appropriate descriptions [10]. But in addition to the technology readiness, organizational readiness factors need to be considered in the process of AI adoption, such as those considered by [PERSON] et al. [11], who compiled five categories with 18 different AI readiness factors (See Table 4) out of exploratory expert interviews as well as findings from scientific and practitioners literature. Aside of the conceptualization framework, they point out the importance that technology readiness and organizational readiness are \"highly interdependent and mutually reinforcing\". But how should this problem be solved by organizations? Which would be the most adequate technologies to start with initial prototypes? Should each organization come up with their own AI map or AI landscape in order to visualize the current state of usage of AI components? In the following, we will show, how a domain specific template can be compiled, which could be reused by individual companies, and help them to spot out unused AI potentials.