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Companies also tap AI to analyse employee sentiment and attrition risk, said SNEF, as well as to benchmark compensation and analyse pay equity.<\/p>\n
Reliance on AI is lowest towards the end of the employment relationship, when dealing with separation and exit matters.<\/p>\n
At this stage, AI is used \u2013 if at all \u2013 mostly to analyse employee attrition data to reduce turnover and support separation planning or disciplinary investigations.<\/p>\n
“Overall, employers are generally more likely to use AI in high-volume processes where AI can complement, rather than replace, professional judgement,” said SNEF.<\/p>\n
Said Mr Kuah: “Employers are already choosing carefully where to use AI, and that is the right instinct.”<\/p>\n
In the United States, makers of AI hiring tools Workday and Eightfold AI face lawsuits contesting how fairly these tools were used to screen candidates. These test cases are being watched for possible implications on AI use in HR processes.<\/p>\n
Asked how concerns about potential bias would be addressed, SNEF chief executive Hao Shuo said employers are generally aware of the laws and their liability for offences of discrimination.<\/p>\n
The survey found that 84 per cent of employers were very or extremely familiar with frameworks such as the Tripartite Guidelines on Fair Employment Practices, Fair Consideration Framework, Model AI Governance Framework for Generative AI, and the forthcoming Workplace Fairness Act, set to come into effect end-2027.<\/p>\n
\u201cWhen they deploy AI in the whole sifting process when CVs come in, employers will make sure that there is no bias in the process,\u201d said Mr Hao.<\/p>\n
He added that jobseekers can also learn how to make their resumes stand out in AI screening, and better understand how AI is deployed in the recruitment process.<\/p>\n
Some employers already have safeguards in place, noted SNEF.<\/p>\n
These include mechanisms to monitor the outcomes of AI tools and make adjustments such as reviewing decision patterns, retraining models, tracking reasons for human intervention and giving feedback to vendors.<\/p>\n<\/div>\n
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