Abstract

As data becomes an increasingly central recours in modern organisations, expectations around how it is collected, processed and analysed continue to grow. In this transformation, AI and particularly generative AI has begun to play a more prominent role. While generative AI shows strong potential to streamline analytics workflows, its rapid development raises questions around practical application, limitations, and the organisational conditions needed to unlock its full value.

This thesis explores the integration of generative AI into analytics workflows within a large globally operating telecommunication company. Through a qualitative research approach and semi-structured interviews, the study investigates where and how generative AI can be implemented to enhance efficiency and decision-making. The analysis applies the technology-organisation-environment (TOE) framework to explore the empirical data from different perspectives and capture the interplay between technological, organisational and external factors. Findings show that generative AI holds significant potential to streamline repetitive tasks such as coding, data structuring and reporting. However, realising these benefits requires clear prompts, high- quality data and users who understand both capabilities and limitations of the tools. Human-AI collaboration emerges as a success factor, highlighting the need for both technical and interpretive skills among users. The study concludes that to fully realise the potential of generative AI, organisations must invest in data governance, foster a culture of innovation, and develop practices that support collaborative interaction between humans and AI.