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  • HR
    August 16, 2018

5 Ways Machine Learning Transforms Employee Engagement

Richard Cheeseman
Director HCM Applications Marketing EMEA
In the first of a two-part series, Andy Campbell, HCM Strategy Director at Oracle, explores how machine learning has the potential to transform the employee experience and engagement.
 
You can’t help but feel some sympathy for machine learning (ML). Too often, it is maligned for being a disruptive threat to employees, automating processes and potentially devouring jobs at an alarming pace. 
 
They need not be alarmed. In truth, ML—whether it’s in the form of natural language processing, conversational agents, or decision support—has a significant role to play in transforming the employee experience, increasing engagement, and improving career progression.
So, let’s examine five ways that HCM solutions incorporating ML can positively impact the lifecycle experience of the employee.
 
 
1. Performance development
 
ML can map employees’ career paths and set them up for career progression, providing guidance on the opportunities and actions others in similar positions may have taken to progress within the organization. ML can support employees with customized training and learning recommendations, based on what other candidates have undertaken to be successful—information that a supervisor may not always provide. With ML, organizations can actually democratize learning and development initiatives for each employee at appropriate timelines. ML can also examine past performance trends of individuals, teams, or departments, allowing steps to be taken to improve future outcomes.
 
2. Remote guidance and learning
 
Remote guidance is an area where ML can make a marked difference. From innovative, interactive learning to real-life simulated scenarios for skill assessment, ML can provide targeted advice for remote problem solving based on past experiences, and the opportunity to collaborate and share advice.
Analytics can also be used to identify areas/personnel where training/reskilling may be necessary or to deliver customized training and development programs for employees.
 
3. Reducing bias in appraisals and career progression
 
One of the many challenges for supervisors during performance reviews is to remain impartial. ML can evaluate performance data without any personal bias for the candidate. The tools can remove human prejudices, building a more equitable, diverse, and unbiased workplace. 
 
4. Managing Rewards and Benefits
 
Benefits and Rewards administration can be tedious, especially in complex, hierarchical organizations. One of the biggest gainers from the use of ML would be this component of employee engagement. For example, ML can help predict which benefits will have the greatest positive impact on the workforce, or how reward strategies correlate with indicators such as performance, leadership effectiveness, or profitability. Modern HCM solutions incorporating ML also enable the seamless integration of innovative employee benefits with traditional incentive packages—creating a novel and compelling employee experience. 
 
5. Streamline and accelerate decision making
 
By removing the manual ‘number crunching’ and facilitating predictive analytics, ML supports more informed and timely decision making. Unhampered by location and time zones, applying ML can enable a more quantifiable, trusted decision regarding employees, based solely on comprehensive content analysis.
Far from being the threat to employees it is often perceived to be, ML has the potential to innovate and transform employee engagement. While it’s important to balance the human factor with technology-enabled solutions, ML moves HCM into a new dimension—one that puts you a step ahead of your competitors.
 
 

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