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KNOWLEDGE HUB

The fourth industrial revolution (4IR) and the rise of automation has had a significant impact on job roles in the manufacturing sector. Research by McKinsey found that although less than 5% of occupations can be automated in their entirety, within 60% of jobs, at least 30% of activities could be automated using 4IR technologies in the near future.

The shift automation is creating in changing roles across the economy calls for a corresponding redevelopment in training and development because as technology evolves, a “growing skills instability” emerges. For example, it is likely that half of all employees will require significant retraining in the next ten years in order to do their jobs whilst accommodating new technologies.

For instance, according to research by ING, 59% of Germans will see their employment affected as robotics take over roles across the factory floor, whilst in the US, 47% of current employment is at ‘high risk of computerisation’ due to the emergence of artificial intelligence. Indeed, the Future Skills research undertaken by Stifterverband and McKinsey estimate that in the next five years, Germany alone will need around 700,000 more people with technological skills than are available today and that over 2.4 million workers must be retrained in key skills such as agile working, digital learning, and collaborative methods in order to deliver the potential of the 4IR in the country.

Although workers will need to learn new skills in the digital age, a study by LinkedIn found that of the 50,000 new skills workers will need to deliver the new industrial revolution, many of them are actually soft skills such as creativity, persuasion, collaboration, adaptability and emotional intelligence. It was found that these softer skills were crucial in achieving the best results when utilising technology-focused hard skills that are often most thought of when considering training for the 4IR.

This finding is significant as it has wider implications in planning for the changes effected by the 4IR as a shift in roles and workplaces is also likely to facilitate complex societal effects as automation becomes part of our daily lives.

As an example, consider the effect of the digitisation of banking. The introduction of ATMs actually increased employment within banks, but it changed the role of bank telling almost completely. Instead of discharging money, teller roles now focus on customer service, problem solving and sales. The repetitive low-skilled element of the role was taken by a machine leaving the individual responsible for deployment of an increased level of soft skills the computer is not capable of. As this system became more cost-effective, more bank branches opened and more bank tellers were needed.

If organisations are able to implement relevant training for the 4IR world, they stand to enjoy benefits such as; shorter time to market for new initiatives, more effective use of resources and improved talent retention. They are also likely to be able to offer more fulfilling, meaningful work for both the individual and teams, and companies focusing on skill development for the 4IR age are also likely to gain better financial returns than companies that fail to streamline the organisation.

The concentration of digital skills within an organisation and the ability to leverage these skills for the firm’s benefit is crucial to that business’ success within the 4IR. The central theme to the success of all 4IR training is cultivating a mindset of collaboration between the worker and technology, encouraging the employee to work with the machine or robot in order to achieve their objectives more effectively. For instance, MIT professor Julie Shah, leads the field of research into human-robot interaction and coined the term “superhuman” to explain how it is possible to enable superhuman performance through collaboration with machines. She believes that to be most effective, workforce training must be focused on supporting the resilience of the individual in collaboration with machines. Therefore, training that focuses on technology-based skills that accentuate human abilitiy in collaboration with automated assistants produce a more resilient and effective workforce than training that focuses purely on how to operate the technology.

However, no organisation operates in a vacuum. External factors such as the state of the economy or supporting government policies or initiatives also play a crucial part. For example, the Pledge to America’s Workers initiative is an example of a federal initiative that now has 370 US companies committed to retraining more than 14 million American workers for technology based jobs over the next five years. Whilst in Germany, the government’s  High-Tech Strategy is designed to create strong links between science, industry, research and society to stimulate training and upskilling that in turn, provides opportunities and jobs for the future.

Understanding the societal context in which the 4IR operates, and how the effects of the digital revolution have now spread throughout the economy, Germany has also formed the “City of the Future” project, encouraging people in 51 cities to discuss ideas about future urban life with policy-makers and researchers in order to identify skill gaps and model future job roles so that projects to fill these gaps can be rolled out.

So how can this change be best nurtured? An MIT study of the digital economy reveals that occupational leadership, physical and cooperation intensity are the most significant predictors of occupational wage and employment growth. This supports the longstanding belief by economists that setting the right environment for change is reliant on the prevalence of tangible (hard skills and assets) and intangible (soft skills such as management practices) being considered equally. Indeed, the MIT study found that firms with structured management practices are better at hiring the “best people” and retaining them over time. Therefore, when considering how to plan for a future in the 4IR, leading from the front is likely to be the deciding factor of success.

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