International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Digital lone for Work Delegations |
|---|---|
| ABSTRACT | In modern digital work environments, professionals face increasing workloads due to repetitive administrative and decision-making tasks. Existing AI assistants offer limited personalization and lack autonomous task delegation capabilities. This paper presents an AI Digital Clone designed to intelligently automate and delegate work tasks by mimicking user preferences, communication style, and decision behavior.The proposed system integrates Natural Language Processing, task orchestration logic, and external service APIs to execute tasks such as email drafting, scheduling, and workflow management. A modular backend architecture ensures scalability and secure delegation, while the AI engine enables context-aware decision-making . Experimental evaluation shows improved task completion efficiency, reduced response time, and enhanced user satisfaction. The system demonstrates the feasibility of AI-driven digital clones for real-world work delegation scenarios. |
| AUTHOR | PROF.YASHASWINI H M, BHAGYASHREE EDLURI, DIVYA SHREE P, DRUVA M, HARSHITHA K G S Department of CSE, Dr. Ambedkar Institute of Technology, Bengaluru, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312086 |
| pdf/86_Digital lone for Work Delegations.pdf | |
| KEYWORDS | |
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