Daniel Maier
Abstract:As a result of current digitalization of society, organizations have been increasingly investing in Artificial Intelligence (AI) technologies in recent years. AI describes the self-learning ability and capability of making humanintervention- independent decisions of digital technologies which offers immense potential in practical business application by realizing higher automation, cost reduction and increased customer satisfaction. Especially the real estate sector is heavily influenced by AI enabled preventive maintenance and failure analysis capabilities. The question of research is whether the AI diffusion within the company and the maturity of the applied AI solutions can already be measured. Furthermore, it raises the question if this extent and level is quantifiable and whether the different measurement approaches allow comparisons with other organizations and institutions. This study is therefore in the context of current research in the field of digital technologies and can be assigned to the area of information systems (IS) research. To answer the research question, the relevant scientific IS literature is examined and linked to various economic theories, such as the Resource Based View (RBV), the Dynamic Capability View (DCV) and the Technology Readiness Index (TRI), the Technology Readiness Level scale (TRL), the Upper Echelon Theory (UET) and the golden triangle. By doing so, the findings of the primary technology-intensive IS literature is combined with the findings of the organization-focused field of strategic management (SM) research enabling a holistic and multifaceted overview about the topic. As a result, it can be stated that there are already various approaches that allow a qualitative self-evaluation of the organizations' Artificial Intelligence maturity, while in parallel there is no measurement procedure available that allows holistic, comparative, and bias-free comparisons. There is therefore a lack of quantitative approaches to measure AI maturity and a lack of objective criteria to examine the AI maturity of an organization.
Key words: Artificial Intelligence; AI; AI Maturity; Digitalization; Digital
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