For decades, companies have relied heavily on selection tools like psychometrics (e.g. ability tests, personality assessments, work preference inventories), competency-based assessments (e.g. situational judgment tests), and behavioural-based interviews to find the perfect candidate for a vacant position. However, these tools fail to capture a complete, unbiased view of our available talent pool. Even worse, they are shockingly ineffective at predicting who will actually thrive in a job. The era of these traditional assessments is, however, coming to an end. After years of steady progress, AI is poised to massively disrupt the talent assessment space and will slowly start rendering traditional psychometric approaches to finding the “perfect candidate” obsolete.

But how will AI transform the talent assessment space and what trends should organisations keep an eye out for in 2024? Current developments in the market indicate that AI will reshape talent acquisition, talent management, and talent development in 14 ways in 2024.

It's clear that AI is not just a tool; it is the architect of a new narrative where organisations embracing these practices will not just evolve, they will lead! The future is dynamic, and those who navigate it with AI as their ally will not only attract and retain top talent but will sculpt a future where innovation, efficiency, and human potential converge harmoniously.

References

França, T. J. F., São Mamede, H., Barroso, J. M. P., & Dos Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4).

Hewage, A. (2023). Exploring the Applicability of Artificial Intelligence in Recruitment and Selection Processes: A Focus on the Recruitment Phase. Journal of Human Resource and Sustainability Studies, 11(3), 603-634.

Matz, S., Teeny, J., Vaid, S. S., Harari, G. M., & Cerf, M. (2023). The Potential of Generative AI for Personalized
Persuasion at Scale. PsyArXiv. https://doi.org/10.31234/osf.io/rn97c

Pargent, F., Schoedel, R., & Stachl, C. (in press). Best Practices in Supervised Machine Learning: A Tutorial for Psychologists. To appear in Advances in Methods
and Practices in Psychological Science. PsyArXiv: https://doi.org/10.31234/osf.io/89snd

Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2023). AI Psychometrics: Using psychometric inventories to obtain psychological profiles of large language models. OSF preprint: https://osf.io/preprints/psyarxiv/jv5dt

Wang, X., Jiang, L., Hernandez-Orallo, J., Sun, L., Stillwell, D., Luo, F., & Xie, X. (2023). Evaluating General-Purpose AI with Psychometrics. arXiv preprint arXiv:2310.16379.