AI-driven solutions impact on technical support engineers : Xend Finance - case study

Abstract

This thesis looks at how artificial intelligence (AI) is being used in technical support, with Xend Finance as the case study. The research focuses on how AI tools are changing daily workflows, employee responsibilities, and overall efficiency in support operations. Theories that highlight both the technological and organisational factors needed for successful adoption of AI tools in technical support were discussed. I collected data through a questionnaire distributed to 17 employees from different roles within the organisation, including technical support engineers, team leaders, and cross functional staff. The findings indicate that AI is already well embedded in daily operations, particularly in automating repetitive tasks, improving ticket handling, and identifying recurring technical issues. This has allowed engineers to dedicate more time to complex and high value problem solving. However, My study also highlights challenges such as limited trust in AI outputs, insufficient training, and the need for employees to adjust to changing job responsibilities.

Based on these insights, my thesis recommends a structured approach to AI adoption which includes role-specific and practical training, encouraging staff to critically evaluate AI outputs, positioning AI as a supportive partner rather than a replacement, and ensuring continuous leadership support. This study concludes that the most effective adoption takes place when AI and human expertise are balanced, enabling operational efficiency while preserving the central role of human judgment in technical support.