As artificial intelligence (AI) becomes an increasingly central force in the financial technology landscape, its integration into Software-as-a-Service (SaaS) platforms promises to transform how financial institutions operate. Yet, despite a growing interest in AI-powered tools, adoption among financial institutions remains limited and inconsistent. This thesis investigates the underlying reasons for this hesitation, exploring the decision-making dynamics that lead institutions to either embrace or reject AI within fintech SaaS environments.

Drawing on a qualitative research design, this study incorporates insights from both SaaS providers and financial institutions through semi-structured interviews. The findings show that adoption is shaped by more than just technological capability: organisational inertia, regulatory ambiguity, and risk perception are major barriers on the institutional side, while SaaS providers often fail to align their solutions with institutional processes, constraints, and strategic priorities. The study identifies a set of tangible practices for both sides to address these misalignments, including collaborative onboarding strategies, modular tool development, clearer communication of AI limitations, and the institutionalisation of cross-functional technology evaluation processes.

By framing adoption as a co-owned challenge, the research contributes to both the theory and practice of AI-driven financial services. For providers, it offers actionable guidance to improve service models and product-market fit. For financial institutions, it outlines how internal processes can be adapted to engage more effectively with emerging AI solutions. Ultimately, the thesis advances a nuanced understanding of adoption not as a binary event, but as a phased, relational process requiring mutual adaptation across organisational boundaries.