Impact of Artificial Intelligence on Business Decision-Making Processes: A Case Study of Small and Medium Enterprises

Thesis Abstract

Abstract
The integration of artificial intelligence (AI) technologies in business operations has revolutionized decision-making processes across various industries. This thesis explores the impact of AI on business decision-making processes, with a specific focus on Small and Medium Enterprises (SMEs). The study examines how SMEs can leverage AI capabilities to enhance their decision-making efficiency, accuracy, and competitiveness in the dynamic business landscape. Through a comprehensive case study approach, this research investigates the practical implications of AI adoption in SMEs and provides valuable insights into the opportunities and challenges associated with integrating AI technologies into decision-making frameworks. The research methodology involves a mixed-methods approach, combining qualitative and quantitative data collection techniques to gather comprehensive insights from SMEs that have implemented AI solutions in their decision-making processes. The study encompasses a sample of SMEs across different industries, allowing for a diverse and representative analysis of the impact of AI on business decision-making. The literature review delves into foundational concepts of AI, decision-making theories, and the existing research landscape on AI adoption in business settings. By synthesizing current knowledge and identifying gaps in the literature, this review sets the stage for understanding the theoretical underpinnings and practical implications of AI in SME decision-making. Findings from the research highlight the transformative potential of AI technologies in enhancing the speed, accuracy, and strategic relevance of decision-making processes within SMEs. The analysis reveals how AI-driven insights can empower SMEs to make data-driven decisions, optimize resource allocation, and capitalize on emerging market opportunities. Moreover, the study uncovers the key challenges faced by SMEs in implementing AI solutions, including issues related to data privacy, skills gap, and organizational resistance to change. In conclusion, this thesis underscores the critical role of AI in reshaping business decision-making paradigms for SMEs, offering a roadmap for successful AI integration and maximizing the benefits of intelligent technologies. The research provides practical recommendations for SMEs looking to leverage AI capabilities effectively, emphasizing the importance of strategic alignment, continuous learning, and proactive adaptation to thrive in a rapidly evolving business environment. By shedding light on the transformative power of AI in SME decision-making, this study contributes to the body of knowledge on AI adoption in business contexts and offers actionable insights for practitioners, policymakers, and academic researchers.