Thesis Abstract

Abstract
The rapid advancement of technology has revolutionized the way businesses operate, providing numerous opportunities for growth and efficiency. Artificial Intelligence (AI) is one such technology that holds great potential for enhancing decision-making processes in small businesses. This thesis explores the implications of implementing AI in small business decision-making and the impact it has on organizational performance. The study begins with an introduction to the topic, highlighting the importance of AI in the contemporary business landscape. The background of the study delves into the evolution of AI and its applications in various industries, emphasizing the benefits it offers to small businesses. The problem statement identifies the existing challenges faced by small businesses in decision-making and how AI can address these issues. The objectives of the study focus on investigating the effectiveness of AI in decision-making, evaluating its impact on business performance, and identifying key success factors for AI adoption in small businesses. The limitations of the study are acknowledged, including potential constraints in data availability and the generalizability of findings. The scope of the study outlines the specific focus areas and industries that will be analyzed. The significance of the study lies in its contribution to the existing literature on AI in small business decision-making and its practical implications for business owners and managers. The structure of the thesis is outlined, providing a roadmap for the reader to navigate through the various chapters and sections. Definitions of key terms related to AI and small business decision-making are provided to ensure clarity and understanding throughout the thesis. Chapter two presents a comprehensive literature review, covering ten key themes related to AI adoption in small businesses. These themes include the benefits of AI, challenges of implementation, ethical considerations, and case studies of successful AI integration in small businesses. Chapter three details the research methodology employed in the study, including the research design, data collection methods, and analysis techniques. The chapter also discusses the sample selection process, data validation procedures, and ethical considerations. Chapter four presents the findings of the study, analyzing the impact of AI on decision-making processes in small businesses and its implications for organizational performance. The chapter provides in-depth discussions, supported by empirical evidence and case studies. Chapter five concludes the thesis by summarizing the key findings, implications for practice, and recommendations for future research. The conclusion highlights the significance of AI in small business decision-making and its potential to drive innovation and growth in the business sector. In conclusion, this thesis contributes to the growing body of knowledge on AI in small business decision-making and provides valuable insights for business owners, managers, policymakers, and researchers. The findings of this study have the potential to inform strategic decision-making processes and enhance the competitiveness of small businesses in the digital age.

Thesis Overview