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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | AI Enabled Effective Employee Engagement framework leading to productivity & Retention |
| Authors Name: | I V S Ranganath , Dr. N V J Rao , Dr. A Niharika, |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217460
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| Published Paper Id: | IJNRD2404221 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | In today's dynamic workplace landscape, organizations are continuously seeking innovative strategies to enhance employee engagement, improve productivity, and retain top talent. Traditional approaches to employee engagement often fall short in addressing the evolving needs and preferences of modern workforce demographics. However, recent advancements in artificial intelligence (AI) present unprecedented opportunities to revolutionize employee engagement practices. This paper proposes an AI-enabled employee engagement framework designed to optimize productivity and increase retention rates within organizations. Leveraging AI technologies such as natural language processing, machine learning, and sentiment analysis, the framework aims to personalize engagement strategies, identify key drivers of employee satisfaction, and predict potential turnover risks. By harnessing real-time data and insights, organizations can tailor interventions and initiatives to meet the unique needs of their workforce, thereby fostering a culture of continuous improvement and growth. The effectiveness of the proposed framework is demonstrated through case studies and empirical research conducted across various industries. Results indicate significant improvements in employee satisfaction, productivity, and retention rates following the implementation of AI-driven engagement initiatives. Moreover, the framework offers scalability and adaptability, enabling organizations to navigate complex challenges and uncertainties in today's competitive business environment. Overall, this paper contributes to the growing body of literature on AI in human resource management by presenting a comprehensive framework for enhancing employee engagement and driving organizational success. By embracing AI technologies, organizations can cultivate a culture of engagement, empower their workforce, and achieve sustainable growth in the digital age. |
| Keywords: | 1. AI-enabled employee engagement 2. Productivity enhancement 3. Retention strategies 4. Manufacturing industries 5. Telangana State 6. Artificial intelligence adoption 7. Workplace dynamics 8. Predictive analytics 9. Personalized engagement initiatives 10. Organizational effectiveness |
| Cite Article: | "AI Enabled Effective Employee Engagement framework leading to productivity & Retention", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b946-b955, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404221.pdf |
| Downloads: | 00033 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404221 Registration ID: 217460 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: b946-b955 Country: Hyderabad, Telangana, India Research Area: Management Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404221 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404221 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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