AI in Investment Decision-Making : Navigating the Use Cases, Perception and Adoption Barriers in Finance

Sammanfattning

The fast growth of artificial intelligence (AI) is transforming industries all over the world, and the financial sector is no exception. AI has the potential to reshape and potentially disrupt traditional practices in the finance world through its ability to make precise decisions and uncover new insights from complex data. However, despite significant advancements in AI algorithms, the adoption of AI in finance remains cautious. This thesis investigates why there is such a disconnect between the sophisticated models proposed in academic literature and the limited evidence of their practical use. This study turns to the professionals working in the field, instead of relying on theoretical assumptions. The aim is to uncover real-world insights into what is slowing down adoption, moving beyond speculation toward an increased understanding by exploring how financial practitioners perceive and apply AI in their day-to-day investment processes. Through a convergent mixed methods design, this thesis draws insights from relevant literature as well as from 25 survey responses and five semi-structured interviews with professionals in asset management. This includes respondents from sectors such as venture capital, equity analysis, and investment banking. The findings reveal a gap between AI’s capacity to improve workflow efficiency and the lack of trust in its outputs for important investment decisions. AI is widely used for administrative tasks such as document summarisation and internal communication but still has a long way to go to have a significant role in decision- making practices within finance. Key barriers to adoption include concerns around explainability, regulatory compliance, and complexity of integrating the technology. These findings highlight that though financial professionals show an intention to use AI, actual adoption is often shaped by the models’ capabilities and trust in the technology. By bridging the gap between the potential of these systems and the realities faced by financial professionals, this thesis contributes to a deeper understanding of AI’s evolving role in investment management and provides insights for stakeholders seeking to explore the adoption of AI within the financial sector as well as developers creating AI-models for the industry.