HR Tech Update: Optimising workflow with machine learning

For every HR challenge today, there is a technological solution aimed at tackling the issue.
Machine learning is playing an increasingly important role in HR. Deloitte’s 2019 Global Human Capital Trends found that 80% of those surveyed predicted growth in the use of cognitive technologies, including machine learning.
Machine learning is a subset of artificial intelligence (AI) that focuses on providing computers with the ability to learn from data, without being explicitly programmed to do so. Machine learning algorithms build mathematical models based on sample data to make predictions or decisions, rather than following rules written by programmers.
It can help automate the process of identifying and recruiting talent, helping organisations keep up with the pace of change, while ensuring that they have the right people to meet their business needs.
Machine learning can also be used to identify patterns in data that would be difficult for humans to spot. For example, it can be used to analyse CVs to identify the most suitable candidates for a role, or to monitor employee communications to identify potential issues before they become problems.
Organisations that are using machine learning in HR are seeing significant benefits. According to Oracle research, over half of the companies it surveyed are using AI in HR.
Machine learning helps reduce recruitment times and organisations can make better use of their data to improve decision-making.
In recent years, consumer goods company Unilever has been using AI and machine learning for this very purpose. The company’s AI scans candidates’ facial expressions, body language and word choices, checking them against traits considered to be predictive of job success.
The system scans graduate candidates’ facial expressions, body language and word choice and checks them against traits that are considered to be predictive of job success. Vodafone, Singapore Airlines and Intel are among other companies to have used similar systems.
Machine learning is also providing new insights into employee behaviour and performance, which can help organisations identify and address issues before they become problems.
Financial services firm JPMorgan used machine learning to survey employee behaviour and identify “rogue employees”. Meanwhile, tech giant Microsoft uses machine learning to monitor employee productivity.
Platforms like Workday, Dayforce, UltiPro, and many more offer solutions that utilise machine learning to improve HR workflow.
Workday’s Workday Human Capital Management allows HR leaders to understand people’s skills and build talent management programs to improve employees’ well-being. Dayforce offers talent intelligence and workforce management to streamline HR processes. Lastly, UltiPro allows HR leaders to optimise their labour and talent management.
However, machine learning is not without its challenges.
One of the key challenges is ensuring that data is of sufficient quality to train the algorithms effectively. In addition, there are concerns about the potential for bias in machine learning algorithms, and also on the lack of transparency around how these algorithms work.
Another challenge is that machine learning requires access to large amounts of data. This can be a problem for organisations that do not have the data, or the resources to collect it.
There are also concerns about the potential for machine learning to replace human judgement altogether. While machine learning can automate many tasks, some tasks will always require human judgement and expertise.
Machine learning is a powerful tool that can help organisations to improve the efficiency of their HR processes. However, it is important to be aware of the challenges that come with using machine learning and to put in place safeguards to ensure that data is of sufficient quality and that algorithms are transparent and unbiased.

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Chief of Staff Asia