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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | AI-Based Employee Performance Prediction and Promotion Role Recommendation System |
| Authors Name: | Mrs. A Gomathi, M.E , Bharathi Raja A , Bhuvanesh D , Jayaraj J , Kaveen S |
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IJRTI_211418
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| Published Paper Id: | IJRTI2604124 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Employee performance evaluation is a cornerstone of organizational success, as it directly influences critical decisions such as promotions, salary increments, employee retention, and long-term workforce planning. Despite its importance, traditional performance evaluation systems continue to rely heavily on manual processes and subjective judgment. These conventional methods often introduce bias, lack consistency, and fail to capture the dynamic nature of employee performance. As a result, organizations frequently encounter challenges such as unfair promotion decisions, underutilization of talent, and decreased employee motivation.In recent years, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) has opened new avenues for transforming organizational processes. These technologies enable the analysis of large volumes of structured and unstructured data, facilitating the identification of patterns and trends that are not easily observable through manual analysis. By leveraging these capabilities, organizations can move towards more objective, data-driven decision-making frameworks.This paper presents an AI-Based Employee Performance Prediction and Promotion Role Recommendation System designed to address the limitations of traditional evaluation methods. The proposed system utilizes machine learning algorithms to analyze various employee-related parameters, including attendance records, productivity metrics, task completion rates, performance feedback, and skill profiles. These inputs are processed to generate predictive insights regarding employee performance.Based on these predictions, the system incorporates a recommendation engine that suggests appropriate promotion roles aligned with both employee capabilities and organizational requirements. The system architecture is based on a three-tier model consisting of a presentation layer, application layer, and data layer, ensuring scalability, modularity, and ease of maintenance. |
| Keywords: | Artificial Intelligence, Machine Learning, Employee Performance Prediction, Promotion Recommendation System, Human Resource Analytics, Predictive Analytics, Classification Algorithms, Data Preprocessing, Decision Support System, Explainable AI, Employee Evaluation, Workforce Management. |
| Cite Article: | "AI-Based Employee Performance Prediction and Promotion Role Recommendation System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a907-a921, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604124.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2604124
Registration ID:211418
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a907-a921 Country: -, -, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604124 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604124 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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