This thesis explores the integration of Artificial Intelligence as a Service (AIaaS) into existing systems, focusing on handling challenges related to unclear data processing and complex integration. The study examined existing research to understand current integration practices and ensure alignment with established standards. Based on this research, an integration architecture was designed and emphasizes two key factors: ensuring the system works as expected (functional suitability) and ensuring different parts of the system can communicate smoothly (interoperability). The integration architecture was designed to simplify communication between different parts of the system, making sure they all work together effectively. It also helps reduce the complications that often come with integration. This mutual reinforcement between functional suitability and interoperability implies coherent outcomes and establishes an environment that fosters smooth communication among system components. The practical implications of this research are exemplified through the implementation of the proposed architecture within the Gokind platform, resulting in positive outcomes. The transition from manual receipt verification to automated receipt recognition using the Google Vision Application Programming Interface (API) showcases accelerated processing times, scalability, and efficient resource allocation. Despite achieving an impressive 90% accuracy rate, the study identifies areas for potential improvement, advocating for ongoing refinement. While the study successfully navigates the challenges related to Artificial Intelligence as a Service (AIaas) integration, it acknowledges certain limitations, such as the potential for exploring varied AIaas providers and environments and the essential consideration of security aspects. Moreover, future research avenues are suggested, including variance analysis across AIaas classes, comparative studies among providers, fortified security measures, and comprehensive exploration of architectural attributes’ impact.

Title: The interaction between humans and machines: A case study on employee job satisfaction during the implementation of artificial intelligence in the workplace Background & problem discussion: As companies adopt new technologies, ensuring job satisfaction is a key for smooth transitions. AI revolutionizes work methods by automating tasks, enhancing efficiency, and requiring new skills. However, AI integration raises concerns about its impacts on roles and satisfaction. While boosting productivity and efficiency, AI can displace human tasks, causing fear and resistance to the new technology. Balancing efficiency with maintaining human expertise is vital, as it affects motivation and engagement in the work. Understanding AI’s impact on job satisfaction is essential, as it significantly influences employees overall experience. Purpose: The purpose of the study is to create an understanding of the employees’ job satisfaction when implementing AI technology in the workplace. Method: This study uses a qualitative research method to understand how AI implementation affects employee job satisfaction at Scania in Oskarshamn. The method focuses on gaining in-depth insights from the employees’ perspective through semi-structured interviews and observations. A case study design was chosen to analyze detailed aspects of AI use within the company. To ensure the reliability, trustworthiness and confirmation of the study, a transparent research process was followed with feedback to the participants. Findings & conclusion: The results showed that both intrinsic and extrinsic involvement were important for job satisfaction. Employees with strategic roles saw AI as an opportunity for improvement, while operational employees felt secure in the use of AI. The study emphasized the importance of fostering an inclusive work culture to ensure positive attitudes towards change and sustained job satisfaction. The conclusion provides both a practical and theoretical contribution for understanding job satisfaction when using AI-technology.

Artificial intelligence (AI) is a fast-paced technology which can be found in different organizations, including the public sector of Sweden. This advanced tool implies many new work processes and an executive of sufficient basic information for such implementation. While finding a usage within the public sector, a problem occurs around how to and what is needed regarding the integration and processes for a complete normalization of the AI usage. Based on the following research question “What are the main challenges for public sector organizations when it comes to normalizing the use of AI in daily tasks?”, the aim of this thesis is therefore to investigate the key obstacles hindering the integration of AI tools into routine tasks. Through eight semi-structured interviews grounded in the Normalization Process Theory (NPT), the study explores theoretical frameworks surrounding AI, which includes generative AI, and an examination of AI implementations within the public sector context. The conclusion of this thesis reveals several obstacles preventing a complete normalization of AI within the public sector. These include the absence of clear guidelines regarding AI usage, lack of legitimacy for AI tools in current workflows, insufficient competence and development opportunities, and limited resources for AI advancement and utilization. These insights show the many challenges the public sector encounters in embracing AI, and furthermore a need for comprehensive strategies to address these obstacles to facilitate the seamless integration of AI technologies into daily operations.