What value does machine learning bring in the workplace context and how will it impact employees’ role(s)?
Nowadays, the speed of technological breakthroughs has no historical precedent. Modern technology has paved the way for a new industrial age, with artificial intelligence (AI) and machine learning among the driving forces behind innovation.
But the future is yet to deliver so much more. Over the next years, the pace of progress will not just continue, it will speed up exponentially. There will be many advancements in AI, robotics, nanotechnology, biotechnology and quantum computing. And businesses need to recognize and capitalize on the opportunities these developments present. As a matter of fact, business investment in AI will grow by 300% until the end of 2017, Forrester predicts. No wonder that almost every major tech company already has an active AI program in place. This is proof that such technology has tremendous potential to transform companies and significantly increase productivity.
Inevitably, some voices take issue with such optimistic forecasts. While it now seems that AI has finally come out of the shadows, not the same holds true about answers to technology versus human labour debates. These are still front page news. Business leader or simple employee? We are all aware that technological developments have provided us with a sense of directionality. But, on the other hand, these changes will have a significant impact on everyone everywhere. In both personal and professional lives.
Questions around the potential impact of machine learning on the workplace are inevitable. Will machine learning take over some of your repetitive tasks? Will you lose your job to machines at some point? How can machine learning make you a better manager? What opportunities does it create for your HR strategies? Irrespective of the benefits or challenges it brings, all these questions can be summarized into one. What value does machine learning bring and how will it change the role that humans play in the workforce? Here are some potential answers:
Recruitment and Selection Hiring Process
Do you spend a lot of time and money in finding a good candidate for an open job position? Machine learning recruitment can help you limit such efforts. The average cost per hire is USD 4,129 and it takes 42 days to fill an open position, according to the Society of Human Resource Management.
Machine learning can automate the process of attracting, assessing and selecting candidates. One way to achieve that is by making use of data gathered from social media activity, employee history and employer information.
Employee Engagement
Do you feel unhappy at work? Do you plan to quit soon but keep it to yourself until you find a better position first? This may not come as good news. Evaluating employee satisfaction and performance via machine learning algorithms? No longer an unlikely scenario for employers. Companies can now use AI to predict employees’ actions by analyzing their past work performance and inquiries.
Machine learning can assess employees’ individual workstyle via a series of tools and data customized to optimize performance. Yet, its added potential does not end here. Machine learning can also boost employee engagement by helping improve the company culture. By enabling employees to work more efficiently, machine learning can reduce their frustrations. People will feel more motivated and will have the time to engage in more purposeful activities.
Business Operations
Businesses across every industry are now implementing machine learning applications to automate processes. For example, companies can transfer repetitive data-rich tasks from employees to virtual robots. Machine learning can replace back-office tasks in accounting, finance, marketing and sales operations. It can also add value to risk and fraud management, supply chain and healthcare processes by building various models.
Customer Service and Retention
For businesses, being able to predict customer behaviour has many advantages. Sentiment analysis and other machine learning techniques enable companies to better address customer feedback or anticipate their queries. They can also quantify customer loyalty through feedback metrics or by analyzing collective patterns of similar customers.
Businesses are now using machine learning to gain valuable insights previously out of reach. However, the belief that machine learning is all about automation and eliminating human input still persists in the minds of many. Yet, to move forward, it is good to keep in mind all areas mentioned above. If you want to keep up with your own times, turn machine learning into a focus of research within your company.
And, no matter the approach, do not forget about the strategy. “Without strategy as a starting point, machine learning risks becoming a tool buried inside a company’s routine operations: it will provide a useful service, but its long-term value will be limited to an endless repetition of ‘cookie cutter’ applications such as models for acquiring, stimulating, and retaining customer”, as research company McKinsey suggests.
