Critical Review of (Alrumi, 2023): Harnessing the Power of Artificial Intelligence to Improve Management Information Systems

1. Introduction

(Alrumi, 2023)presents a timely review exploring how Artificial Intelligence (AI) can improve the performance and decision-making capabilities of Management Information Systems (MIS). The article’s central research question investigates how AI can enhance MIS while overcoming integration, ethical, and technical challenges. While the review offers valuable sectoral insights, its narrow literature base and limited critical analysis reduce its academic rigor.

2. Summary of Article

The article aims to assess AI’s impact on MIS across industries through a systematic literature review. Using the PRISMA method, 29 papers were selected—mainly recent publications—highlighting AI applications in government, healthcare, manufacturing, marketing, and customer management. The author concludes that AI has vast potential to enhance MIS but stresses the need for thoughtful integration, particularly emphasizing human-AI collaboration.

3. Critical Evaluation

3.1 Research Significance and Originality

The paper addresses a highly relevant topic, especially in light of AI’s growing use in business systems. Its novelty lies in the cross-sectoral synthesis, bridging gaps between MIS theory and AI application. However, the research questions are not groundbreaking, and the article’s contribution is more descriptive than theoretical.

3.2 Methodology

The use of PRISMA and a structured review process is commendable, but the methodology has key limitations. Only Google Scholar was used for sourcing papers, limiting comprehensiveness. Additionally, search terms and quality assessment criteria were not disclosed, reducing transparency and reproducibility.

3.3 Argumentation and Evidence

The article is well-structured, with sector-specific examples supporting the argument that AI can optimize MIS. However, it relies heavily on summaries of individual studies without synthesizing contrasting findings or critically evaluating the evidence. As a result, the analysis lacks depth and often avoids nuanced discussion of conflicting results or implementation failures.

3.4 Theoretical Contribution

The review does not propose new frameworks but supports existing theories such as collaborative intelligence and human-AI augmentation. It identifies research gaps—particularly the lack of studies on long-term AI outcomes in MIS—offering a useful starting point for future research.

3.5 Practical Implications

Alrumi’s work has strong practical relevance. It offers insight into how AI can enhance decision support, predictive maintenance, and service personalization across sectors. However, the lack of concrete recommendations or a unified framework limits its utility for practitioners seeking actionable strategies.

3.6 Ethical Considerations

While ethical challenges such as bias and data privacy are briefly mentioned, the article lacks an in-depth discussion on governance or mitigation strategies. This omission is significant given the ethical sensitivity of AI use in public and healthcare sectors.

4. Conclusion

(Alrumi, 2023) provides a timely overview of AI’s transformative potential in MIS. Its strengths lie in its sectoral breadth, practical relevance, and recognition of the need for human-AI collaboration. However, methodological constraints, limited critical analysis, and insufficient ethical engagement reduce its overall impact. Future research should aim for broader source inclusion, outcome-based evaluations, and deeper integration of ethical and governance considerations to guide responsible AI use in MIS.

5. Reference

Alrumi, A. R. (2023). Harnessing the Power of Artificial Intelligence to Improve Management Information Systems. International Journal for Quality Research, 18(1), 115–128.

(Alrumi, 2023)