Introduction
The integration of artificial intelligence (AI) in recruitment is a major advancement that promises to optimize processes and transform traditional practices.
But is it really for the better?
What are the tasks where AI is definitely a major asset and which are those where it is of no use or even counterproductive?
Between myth and reality, this article highlights the true place of humans and machines within the recruitment process!
Myth 1: AI will replace recruiters
It is thought that AI…
Will replace human recruitment functions, making recruiters and human resources professionals obsolete.
Reality:
Far from replacing recruiters, AI assists them by automating administrative tasks and allowing them to focus on more strategic aspects of recruitment, such as candidate engagement and workforce planning. A Deloitte study reveals that AI, rather than eliminating HR jobs, has helped create new ones, focused on more analytical and interpersonal skills (Deloitte Insights).
Here is an excerpt from this report:
Our 2024 Global Human Capital Trends report reveals that the human factor is the gateway between knowing the changes shaping the future of work and the steps to actually progress in implementing these changes to achieve positive outcomes. This year’s global survey responses (over 14,000 participants in 95 countries), clearly show that as the boundaries of work blur, the unique human capabilities, like empathy and curiosity, become more important.
Myth 2: AI is completely impartial
It is thought that AI…
provides a completely neutral and unbiased recruitment method.
Reality:
AI is designed by humans and can therefore inherit their biases. According to research from Carnegie Mellon University, recruitment algorithms can develop unintended biases if the training data is not properly balanced. It is crucial that companies continuously monitor and adjust their AI systems to ensure fairness (ACM Digital Library).
You can find a very well-written article on the different biases of AI in recruitment on the ACM Digital Library website (see link at the bottom of the article).
Myth 3: AI makes recruitment decisions faster and always better
It is thought that AI…
speeds up recruitment by making more accurate and efficient decisions than humans.
Reality:
While AI can speed up the selection process by quickly filtering resumes, it does not replace the human judgment necessary to assess interpersonal skills and growth potential. The best results are achieved when AI and humans work in tandem, each complementing the other’s capabilities.
Myth 4: AI can completely automate the recruitment process
It is thought that AI…
can autonomously manage the entire recruitment process without human intervention.
Reality:
Even with significant advances, AI cannot handle all the subtleties of the recruitment process alone. Human interactions, personal interviews, and understanding of cultural and personal nuances remain essential. Companies must use AI as one tool among others, not as a complete solution.
Myth 5: AI is a plug-and-play tool for recruitment
It is thought that AI…
can be easily integrated into any recruitment process without the need for customization or follow-up.
Reality:
Implementing AI in recruitment processes requires strategic planning, ongoing training, and regular evaluation. AI systems must be customized to fit the specifics of each organization and require regular adjustments to remain effective.
Conclusion
The myths surrounding AI in recruitment are numerous, but an evidence-based evaluation reveals a more nuanced reality. AI has the potential to transform recruitment for the better, provided it is used ethically and complements human skills. For successful integration, a deep understanding and careful management are essential. Of course, regularly challenging both AI and humans is a must!
Companies must stay informed and ready to adjust their approaches to fully leverage the benefits AI can offer.
Deloitte Insights: https://www2.deloitte.com/ca/fr/pages/consulting/articles/2024-global-human-capital-trends.html
ACM Digital Library: https://dl.acm.org/doi/10.1145/3514094.3534151
