UNLOCKING ORGANIZATIONAL POTENTIAL: TALENT MANAGEMENT AS A DRIVER OF INNOVATION IN THE AI ERA Cover Image

UNLOCKING ORGANIZATIONAL POTENTIAL: TALENT MANAGEMENT AS A DRIVER OF INNOVATION IN THE AI ERA
UNLOCKING ORGANIZATIONAL POTENTIAL: TALENT MANAGEMENT AS A DRIVER OF INNOVATION IN THE AI ERA

Author(s): Ema Burić, Admir Čavalić, Faruk Hadžić
Subject(s): Business Economy / Management, Human Resources in Economy, ICT Information and Communications Technologies, Artificial Intelligence
Published by: Ekonomski fakultet, Univerzitet u Tuzli
Keywords: talent; talent management; talent management strategy;

Summary/Abstract: In the context of increasing global competition and rapid technological change, talent management has become a strategic imperative, especially for small and medium-sized enterprises. This paper first explores the importance of talent management and its relevance in a dynamic business environment shaped by digital transformation and artificial intelligence. Through desk research, it is demonstrated that effective talent strategies are becoming essential for innovation, adaptability, and long-term competitiveness. Building on these findings, the paper presents five case studies conducted in 2023 across diverse service enterprises, including banking, retail, IT, hospitality, and market research. The aim was to provide a deeper insight into the actual forms of implementation, practices, and challenges that organizations face in the process of talent management. The results indicate that while the importance of talent management is widely acknowledged, it is rarely implemented as a comprehensive, integrated system. Instead, SMEs tend to focus on isolated activities, often lacking strategic alignment. The paper concludes that in BiH there is a strong need for greater promotion of talent management as a strategic priority. To achieve meaningful and sustainable results, SMEs should move beyond fragmented efforts and adopt a comprehensive talent management model.

  • Issue Year: 2025
  • Issue No: 1
  • Page Range: 332-343
  • Page Count: 12
  • Language: English
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