Artificial Intelligence Methods to Support People Management in Organisations through Innovation

Abstract

Organizations are shifting from individual roles to team-based structures, creating new challenges in team management. This study proposes a people management system to improve team effectiveness through better team formation, evaluations, and performance tracking. A review of research in psychology and computer science shows that individual traits impact team outcomes. The study outlines methods to assess personality and competencies, which inform a predictive model for team performance. It introduces the Synergistic Team Composition Problem (STCP) and provides two algorithms—one for small teams and another for larger groups—to optimize team formation. Additionally, it presents the Collaborative Judgment algorithm to reduce evaluation bias by incorporating peer feedback. Empirical tests support the benefits of these approaches in enhancing team effectiveness.

Introduction

Artificial Intelligence (AI) is transforming Human Resource Management (HRM) by automating and improving key functions such as recruitment, performance evaluation, and employee development. Traditional HR methods often suffer from inefficiency and bias, whereas AI tools like chatbots, resume screeners, and predictive analytics increase fairness, speed, and accuracy in hiring and talent management. AI enables real-time performance monitoring, personalized training, and data-driven decision-making, which enhances employee engagement and organizational productivity.

The study explores AI’s role in optimizing team composition, reducing biases, and aligning workforce capabilities with business goals. It also emphasizes ethical considerations like data privacy and the need for balancing automation with human empathy. Using a mixed-method approach, including surveys, interviews, and case studies, the research highlights AI’s benefits in creating more objective, continuous performance evaluations and tailored employee development programs.

AI-driven recruitment minimizes subjective bias and predicts candidate success, improving hiring outcomes. In performance management, AI offers continuous feedback, bias reduction, and predictive insights for better workforce planning. For employee development, AI personalizes learning paths based on individual skills and career goals, boosting training effectiveness and retention. Overall, AI integration in HRM promises greater efficiency, fairness, and strategic workforce alignment, while underscoring the importance of ethical implementation and human oversight.

Conclusion

The application of Artificial Intelligence in employee development has proven to be highly effective by offering tailored learning experiences, boosting productivity, and delivering more precise learning evaluations than traditional methods. Successful adoption of AI in HR leads to improved knowledge retention, greater efficiency, lower training costs, and enhanced career planning. Nevertheless, organizations must overcome challenges such as safeguarding data privacy, minimizing bias, ensuring system compatibility, and gaining employee trust. Looking ahead, AI advancements hold promise for even more personalized, ethical, and forward-thinking development strategies that align with organizational growth, employee satisfaction, and flexible career trajectories. It is essential for HR professionals to navigate current obstacles while preparing to leverage emerging AI tools to strengthen workforce development and motivation.

References

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Copyright

Copyright © 2025 Amit Kumar Bhagat, Shyam Shankar Dwivedi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.