Building Better Teams: The Role of AI in Personality Assessment for IT Professionals

Abstract

Introduction: In today’s fast-paced and innovation-driven IT industry, team dynamics play a pivotal role in organizational success. Traditional team-building approaches often fall short due to biases and inefficiencies. To address this, Artificial Intelligence (AI) offers data-driven methods for evaluating personality compatibility, fostering better team cohesion, and enhancing workplace productivity.

Objectives: The study aims to explore the application of AI in personality assessment and team formation within IT organizations. It investigates how clustering algorithms and sentiment-aware analysis can be used to improve personality matching and optimize team composition.

Methods: The research employs the Big Five Personality Test data, applying K-means clustering to identify compatible personality profiles among employees. A dataset of 19,720 responses is analyzed and grouped into ten distinct clusters. The Elbow Method determines the optimal number of clusters, while Principal Component Analysis (PCA) assists in visual interpretation of results.

Results: The framework successfully identified suitable candidates in 87% of team-matching cases within the initial cluster. In the remaining cases, dynamic cluster expansion ensured appropriate matches. The system showed strong scalability and efficiency, clustering 500 records in under 1.2 seconds. Visualizations confirmed meaningful and well-separated personality clusters.

Conclusions: The study demonstrates the efficacy of AI in team building, enabling HR professionals to form compatible teams based on personality traits. The framework is adaptable, scalable, and holds promise for diverse organizational applications, from employee development to conflict resolution.