<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10013786" mdate="2024-08-28 00:00:00"> <author>Arun Prasad Jaganathan</author> <title>Design and Implementation of an AIEnabled Task Assistance and Management System</title> <pages>474 - 478</pages> <year>2024</year> <volume>18</volume> <number>8</number> <journal>International Journal of Economics and Management Engineering</journal> <ee>https://publications.waset.org/pdf/10013786</ee> <url>https://publications.waset.org/vol/212</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>In today&amp;039;s dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper presents an AIenabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on realtime data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decisionmaking. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.</abstract> <index>Open Science Index 212, 2024</index> </article>