The Artificial Intelligence Talent Puzzle: How Company Size Shapes Hiring Patterns in AI

Or Peretz and Maayan Nakash ()
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Or Peretz: Bar-Ilan University, Department of Management
Maayan Nakash: Bar-Ilan University, Department of Management

A chapter in Leading Change in Disruptive Times, 2026, pp 442-453 from Springer

Abstract: Abstract The rapid advancement of Artificial Intelligence (AI) is significantly transforming the global labor market, driving increased demand for professionals with specialized AI competencies. This study explores the relationship between organizational size and recruitment patterns within the AI sector. A dataset comprising 10,968 AI-related job postings was manually compiled from LinkedIn between January and September 2024. Employing text mining, natural language processing, and statistical techniques, the analysis focuses on trends in required experience, seniority levels, and employment types across small, medium, and large enterprises. The findings indicate distinct recruitment strategies: small firms exhibit the greatest diversity in role offerings, spanning all seniority levels and employment types—including full-time, part-time, and contract positions—and provide the most opportunities for entry-level candidates. Conversely, large organizations predominantly seek mid-level professionals with substantial experience, while offering comparatively fewer senior roles. Medium-sized companies demonstrate relatively limited hiring activity, particularly in part-time positions. These results challenge prevailing assumptions about AI talent acquisition and underscore the complex interplay between company size and hiring behavior. The study offers actionable insights for job seekers, human resource professionals, and organizational leaders navigating the evolving landscape of AI employment.

Keywords: Artificial Intelligence; AI Job Market; Talent Acquisition; LinkedIn; Career Development; Company Size (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-19276-9_30

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DOI: 10.1007/978-3-032-19276-9_30

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