Exploring the complexities of AI implementation in organizations - A qualitative study based on expert interviews

Abstract

The recent advancements in artificial intelligence (AI) have led to organizations trying to create business value through AI in business applications. However, the implementations are still complex and multifaceted, causing challenges related to technological, human, and strategic factors. This thesis investigates these complexities through expert interviews with professionals across multiple industries in Finland with the aim of understanding the barriers related to successful AI implementation and revealing strategies to overcome them.

The study focuses on three main aspects of AI adoption: strategic decision-making, technological readiness, and human factors. Organizations face challenges such as data quality issues, integration with legacy systems, employee resistance, and justifying return on investment (ROI). To address these challenges, organizations employ incremental AI adoption strategies, pilot projects, cross-functional collaboration, and employee training programs.

The research draws on two theorems: The Dynamic Capabilities Theorem and the Sociotechnical Systems Theory. It emphasizes the need for adaptability and foresight and focuses on the integration of technology with organizational culture. The findings show that organizations that have flexible strategies and a culture that encourages experimentation are more likely to successfully leverage AI in their business operations.

The results provide practical baseline recommendations for organizations to effec- tively implement AI while ensuring that their AI initiatives align with the organization’s overall business goals, including technological and cultural aspects. Future research could explore measurements for long-term impacts, regulatory influences, and ethical considerations.