Paper Title

AI Enabled Effective Employee Engagement framework leading to productivity & Retention

Article Identifiers

Registration ID: IJNRD_217460

Published ID: IJNRD2404221

DOI: Click Here to Get

Authors

I V S Ranganath , Dr. N V J Rao , Dr. A Niharika,

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

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.

How To Cite (APA)

I V S Ranganath, Dr. N V J Rao, & Dr. A Niharika, (April-2024). AI Enabled Effective Employee Engagement framework leading to productivity & Retention. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), b946-b955. https://ijnrd.org/papers/IJNRD2404221.pdf

Issue

Volume 9 Issue 4, April-2024

Pages : b946-b955

Other Publication Details

Paper Reg. ID: IJNRD_217460

Published Paper Id: IJNRD2404221

Downloads: 000122001

Research Area: Management

Country: Hyderabad, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404221.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404221

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

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Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

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Frequency: Monthly (12 issue Annually).

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