Status : Verified
| Personal Name | Thuta Murali Krishnan, Hemanth Kumar |
|---|---|
| Resource Title | Assessment of the future of Artificial Intelligence in recruitment: a technology foresight through scenario planning |
| Date Issued | 28 March 2025 |
| Abstract | The rapid evolution of Artificial Intelligence (AI) in recruitment is transforming traditional hiring processes, enabling organizations to enhance efficiency, streamline talent acquisition, and improve decision-making. This capstone paper explores the future integration and implementation of artificial intelligence (AI) in recruitment processes, using scenario building a technology foresight methodology. It provides a high-level examination of current AI-driven recruitment initiatives, challenges, and opportunities, envisioning plausible scenarios for AI adoption in talent acquisition practices within the next three to five years. The study identifies critical drivers influencing AI integration in recruitment, including AI-driven automation, technological advancements, organizational readiness, cost implications, acceptance by HR professionals, and potential returns on investment (ROI). It highlights that while AI technologies have significantly enhanced efficiency in recruitment especially in high volume tasks such as resume screening, talent sourcing, and interview scheduling full scale AI adoption varies considerably due to differing levels of organizational readiness, cost considerations, and human acceptance. Three distinct future scenarios were developed to explore future landscapes: 1. Full AI Takeover: Substantial automation across the recruitment lifecycle, significantly reducing human intervention, and improving operational efficiencies, but raising concerns about potential bias, transparency, and reliance on AI algorithms. 2. AI in Handcuffs: AI adoption occurs but faces strong organizational resistance and constraints, resulting in AI serving primarily as a supportive tool with considerable human oversight, thus leading to increased complexity and operational overhead. 3. Stuck in Transition: Organizations hesitant or unable to commit fully to AI adoption, relying heavily on traditional recruitment practices. This scenario results in highe |
| Degree Course | Master of Technology Management |
| Language | English |
| Keyword | Talent acquisition; Recruitment; Human resources technology |
| Material Type | Thesis/Dissertation |
Preliminary Pages
89.43 Kb
Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access