In the rapidly changing recruitment context, the integration of Artificial Intelligence (AI) can without a doubt bring about significant advantages. In our previous blogs, we explored the benefits associated with adopting AI as part of talent management practices and, in particular, how it can streamline time-consuming candidate screening processes and optimise talent assessment and retention. Despite this, research by MIT has indicated that seven out of 10 AI projects lead to limited impact, and that there was a decrease in AI implementation plans from 20% in 2019 to 4% in 20201. Therefore, in this blog we explore the potential barriers to AI implementation and how to address these.

First, although AI, in theory,  can lead to less biased assessment and selection by removing conscious and unconscious human biases from the decision-making process, it is not completely bias-free. More specifically, human bias can be incorporated into machine learning algorithms. This can occur if the training data is biased due to a skewed sample, in which a particular outcome is more often associated with a specific group2. Thus, even well-intentioned AI programmes can be discriminatory. For instance, in the infamous Amazon case it was found that the machine learning algorithm used as part of an AI recruitment programme was biased against women. To address this, organisations have to try and use representative learning data and deliberately ensure that variables such as gender and race are included in algorithms3. Furthermore, research suggests that more diverse engineering teams are less likely to create biased AI algorithms4.

Second, research has indicated that many people distrust AI because they do not understand how it works and are concerned regarding the reliability and accuracy of AI technologies as well as the possibility for discrimination 3, 4. Building upon this, studies with HR professionals have shown that people find AI decisions impersonal and reductionist3. Importantly, such concerns can be addressed by ensuring that people have control over the ultimate decision4. Hence, it is essential that organisations determine how HR professionals can effectively make use of AI-driven recommendations. In this vein, it should be noted that certain human qualities will remain irreplaceable. For instance, human characteristics such as empathy, intuitiveness and emotional intelligence will still be required for cultural fit assessments and rapport building 3,5.

Last but not least, another key concern regarding the adoption of AI relates to its impact on employees’ privacy. By using AI technologies organisations can track employees’ activity in real time and if implemented poorly, AI can lead to an increase in employee stress, burnout and poor mental health4. Therefore, it is essential that organisations are transparent about the purpose of using AI.

Considering these limitations, a comprehensive literature review1 found that in order to effectively adopt AI technologies, organisations need to (i) have a good understanding of the benefits and shortcomings of using AI and develop appropriate strategies and communication mechanisms to ensure that everyone is aware how AI can be effectively used to solve different organisational challenges, (ii) possess the necessary expertise to accurately interpret AI-generated output and link that to wider organisational objectives, and (iii) effectively manage the evolution of AI technology to respond to the dynamic business environments nowadays.


1 Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A. and Truong, L., 2023. Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), p.100899.

2 Mujtaba, D.F. and Mahapatra, N.R., 2019, November. Ethical considerations in AI-based recruitment. In 2019 IEEE International Symposium on Technology and Society (ISTAS) (pp. 1-7). IEEE.

3 Ore, O. and Sposato, M., 2022. Opportunities and risks of artificial intelligence in recruitment and selection. International Journal of Organizational Analysis, 30(6), pp.1771-1782.

4 Schmid, K., and Raveendhran, R., 2022. Where AI Can – and Can’t – Help Talent Management. Harvard Business Review.

5 Oswal, N., Khaleeli, M. and Alarmoti, A., 2020. Recruitment in the Era of Industry 4.0: use of Artificial Intelligence in Recruitment and its impact. PalArch’s Journal of Archaeology of Egypt/Egyptology, 17(8), pp.39-47.