Year: 2025
Type: dissertation
Abstract: As digital transformation reshapes the business environment, enterprises must adopt emerging technologies to maintain competitiveness. One of the most recent innovations in this transformation is Conversational AI (CAI). It has the potential to revolutionise customer service operations by enhancing enterprises’ efficiency and scalability while also improving customer experience. Adopting CAI systems into Enterprise Architecture (EA) presents a complex challenge, requiring enterprises to address technical, operational, and strategic aspects. While the potential of CAI technology is widely recognised, its systematic adoption as part of EA remains underexplored. This thesis examines how enterprises can effectively adopt a CAI system into EA to advance customer service operations in the energy sector. The study adopts a qualitative research approach, comprising a literature review to analyse existing theoretical frameworks and a case study conducted within the Finnish energy company Helen Ltd. The empirical investigation explores key factors influencing CAI system adoption, drawing on semi-structured interviews with IT architects, product owners, AI specialists, and cybersecurity experts of the case company. The primary outcome of this research is a CAIEA Adoption Framework for Customer Service. This framework builds on the known EA layers and their role in the phased AI adoption process. In addition, the thesis focuses more on the technology layer, highlighting its central role in CAI system’s secure and trustworthy operation. Findings indicate that CAI system integrations should be built around business logic, ensuring decision-making accuracy and consistency. Additionally, risk management was identified as a cross-cutting process, extending across all identified EA layers. The study underscores the importance of developing a comprehensive risk management strategy to anticipate and prepare for potential risks.
Source: Aaltodoc (Aalto University)
Author: Tuomela, Anni
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7067043274