Vol. 2 No. 3 (2023): December Special Issue of The QUEST: Journal of Multidisciplinary Research and Development
Articles

Assessment and Optimizing Task Performance through Artificial Intelligence Systems in Hangzhou Ruinan Information Technology Co., Ltd

  • Du Dan Santos,
  • RuthAnn Santos
Du Dan Santos
Hangzhou Ruinan Information Technology Co., Ltd., China
RuthAnn Santos
Nueva Ecija University of Science and Technology
DOI: https://doi.org/10.60008/thequest.v2i3.159

Published 12/30/2023

Keywords

  • Artificial Intelligence,
  • Task Performance,
  • Optimization,
  • Data Quality,
  • User Acceptance,
  • Expertise,
  • Scalability, Efficiency
    • ...More
    Less

How to Cite

Santos, D. D., & Santos, R. (2023). Assessment and Optimizing Task Performance through Artificial Intelligence Systems in Hangzhou Ruinan Information Technology Co., Ltd. The QUEST: Journal of Multidisciplinary Research and Development, 2(3). https://doi.org/10.60008/thequest.v2i3.159

Copyright (c) 2023 The QUEST: Journal of Multidisciplinary Research and Development

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

This research study employs a quantitative-descriptive approach to evaluate and enhance task performance through the utilization of Artificial Intelligence (AI) systems at Hangzhou Ruinan Information Technology Co., Ltd., a key player in the technology hub of Hangzhou City, China. The study comprises four main parts: examining AI system characteristics and provisioning, assessing actual task performance, investigating factors influencing system effectiveness, and identifying challenges impacting performance. Participants include management, IT professionals, and employees working with AI systems, providing valuable insights into system implementation and operation. Data analysis, utilizing weighted mean and verbal description techniques, offers both quantitative and qualitative perspectives on AI's role in optimizing task performance. Findings reveal strong scalability, efficient overall performance, and positive impacts on workflow processes and resource utilization. Security measures, especially in data protection, and usability aspects require attention for further enhancement. The study underscores the importance of reliable data, seamless user adoption, enhanced expertise, and consistent system performance in optimizing task performance through AI within the organization. Conclusions emphasize the need to strengthen security measures, enhance usability, and address areas for improvement. The study underscores the value of AI in achieving the organization's goal of enhancing task performance and productivity. Recommendations include initiatives to enhance data quality, streamline user acceptance, invest in employee skill development, and improve scalability and performance. These recommendations aim to guide Hangzhou Ruinan Information Technology Co., Ltd. in refining its AI systems to drive efficiency and excellence in its operations.

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