Identification of Challenges in the Application of Artificial Intelligence in Facility Management and Highlighting Optimization Potentials

Abstract

Artificial Intelligence (AI) and data collection in facility management bring a variety of improvements, such as increased efficiency, cost control, enhanced user experiences, and more. With targeted data management, this information can be further processed and contribute to the sustainable operation of buildings.  AI currently presents several challenges that prevent its optimization potential from being fully realized. These challenges can be specified across various data domains, such as data quality, data security, and more. Additionally, legal aspects, such as the collection of user data and the associated legal restrictions, play a significant role.

To identify and address these challenges, an Ishikawa diagram is created. The Ishikawa diagram is used to systematically analyze possible problems. This method helps visualize connections, prioritize causes, and identify approaches to solutions. During its application, the problems are first defined, and their impacts are analyzed. Subsequently, the main categories of causes are determined, such as people, machines, methods, and so on. Afterward, the causes are identified through brainstorming, followed by an analysis of these causes. Based on this analysis, appropriate measures can be derived. These measures may include avoidance, mitigation, acceptance, and transfer.

The results are then used to eliminate or minimize the causes of the challenges. The results and corresponding measures are documented and reviewed to determine whether the problem can be resolved or reduced. The outcomes are presented in the form of a table, linked with the respective measures, and the proposed solutions are outlined.