Enhancing Decision-Making in Information Systems with AI tools: A Case Study Analysis

Enhancing Decision-Making in Information Systems with AI tools: A Case Study Analysis

Kafumukache Besa Bwalya

Transport and Telecommunication Institute

Lomonosova 1, LV-1019, Riga, Latvia

kabebwal@gmail.com Keywords: Decision-making, Information systems, AI tools, Enhancing, Case study analysis

Decision-making in information systems plays a crucial role in the success of organizations. To enhance decision-making processes, the use of AI tools has emerged as a promising approach (Raman, S, K. and Phoon, K, C., 1990). By leveraging AI technologies, organizations can gain valuable insights from complex and vast amounts of data, enabling better-informed decisions.

This thesis aims to analyze the effectiveness and impact of AI tools in enhancing decision-making in information systems through a case study analysis (Wamba-Taguimdje, S. et al., 2020). The rapid development of information technology has led to the gradual application of artificial intelligence technology in various fields. According to Lee, the development of artificial intelligence technology has exceeded expectations and has the potential to significantly transform various industries. In the era of big data, AI technology offers opportunities for researchers and managers to improve decision-making processes. According to previous research, AI has the potential to be a game changer in project management by accelerating productivity and increasing project success rates.

This thesis will focus on analyzing the effectiveness and impact of AI tools in enhancing decision-making in information systems through a case study analysis. The proposed framework for this thesis involves examining case studies that demonstrate the use of AI tools in decision-making processes in information systems. As organizations continue to embrace AI tools for decision-making processes, it is crucial to understand their effectiveness and impact. The case study analysis will involve examining real-world scenarios where AI tools have been utilized to improve decision-making in information systems. By analyzing these cases, this thesis aims to provide a comprehensive overview of the benefits and challenges associated with the integration of AI tools in decision-making processes. Additionally, the research will delve into the implications of AI tools on organizational performance and competitiveness, providing valuable insights for practitioners and decision-makers in the field of information systems.

This study will contribute to the existing body of knowledge by offering practical recommendations for leveraging AI tools to enhance decision-making processes in information systems. The research proposed in this thesis will contribute to the existing body of knowledge by providing significant and tangible evidence about the business value of AI-based projects and their impact on firm performance. The research will draw on 5 case studies to examine the influence of AI at both the organizational and process performance levels, emphasizing that AI should be seen as a set of different configurations of IT in various industries rather than as a single technology.

Furthermore, this research aims to provide managers with actionable insights on reconfiguring or adding elements to leverage the benefits of AI and improve organizational performance and profitability.

The research is supervised by Dr.sc.ing., Professor Boriss Misnevs.

References

Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J.R. and Tchatchouang Wanko, C.E. (2020), Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects, Business Process Management Journal, Vol. 26 No. 7, pp. 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411

K.S. Raman 1 et al. (2002) Decision Support Systems in Singapore: Issues in their management and their beneficial contributions, Information & Management. Available at: https://www.sciencedirect.com/science/article/abs/pii/037872069090069T?via%3Dihub (Accessed: 15 March 2024).

Mohammed et al., (2017), An empirical assessment of the link between decision support system (DSS) capabilities, competencies and firm performance. ICMSS '17: Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, DOI: 10.1145/3034950.3034956.

Tue Nhi Tran, Huyen Tan Tran and Quy Nam Nguyen (2023). Leveraging AI for Enhanced Software Effort Estimation: A Comprehensive Study and Framework Proposal. DOI: https://doi.org/10.1109/iccd59681.2023.10420603