4 Findings
5.2 Implications for the Future Workforce
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76 important. Not only for society and the economy, but also for the personal development of each individual (3#3, 4#15, 5#19).
Implementation of life-long learning should already start at a very early stage in the educational system. It is argued that it does not make sense to teach children facts anymore. The change should include the education of students to active based, as well as to more linked and interdisciplinary learning (1#4). New ways of learning, such as e-learning platforms, can help to change learning tools. Schools can therefore offer more personalized teaching (1#4, 5#13, 5#19, 5#20).
Life-long learning helps to deal with social changes and to better meet the challenges of the labour market. It creates self-confidence and can decisively improve the quality of life (2#11). Continued education and life-long learning should be central themes of education policy (5#4, 5#20).
New technologies pose many challenges. For instance, it needs to be sure that humans are a priority (#4) in all of the phases of the technology design; and for the way, in which technology is utilized in the workplace. It has to be assured that special training is available (4#27), and that workers have life-long learning opportunities to gain the skills required for using these technologies. And that they are not just substituted by robots and other forms of new machines (1#24).
Finally, in the context of digitalisation, the field of continuing vocational training is often discussed.
People from occupations with a high “substitutability potential” are mostly threatened by digital change processes. In the worst case, a disruptive loss of jobs is possible. A return to the labour market is also possible, however, as the overall demand for labour will not disappear. But the question is, how quickly and under what conditions this re-entry is possible in individual cases (1#24).
Through a targeted and tailor-made training policy, so-called “mismatch” problems can be reduced by retraining in areas that are less threatened by digitisation. Ideally, such promotion of occupational mobility can even take place with foresight, i.e. before a job loss has actually occurred (Peters, 2017).
5.2.3 Human-Machine Interaction
Especially experts with a background in technology claim that asides “human skills”, digital skills are equally relevant for employees (1#18, 1#24), and therefore highlight the importance of a correctly mixed skillset. This is in line with Daughtery & Wilson (2018) who introduce the “fusion skill”
framework to describe a human-machine symbiosis.
In the future, the right mix of skills is indeed necessary. In an engineering environment, data literacy,
manufacturing skills and handling technological processes are from high relevance, but so are soft
77 skills, especially in leadership and social situations (2#11). A knowledge-based skillset is required, as it needs to be known how technology should be handled and how to react properly to machines:
but according to expert (5#19), not everyone needs to have the full digital skillset, especially if someone is not working in the technology industry. Another opinion refers to the importance of IT skills: the expert argues that at least on the shop floor, not that much work is left for humans, so they need the skills that are adaptable to deal with this kind of changes (1#17).
A discrepancy seems to exist between human emotions and technology (1#5). That is why it is hard for robots to replace humans, when they are interacting with others – both physically and emotionally.
Emotions and empathy are important in the world of work in several aspects. First, emotional and empathic behaviour are still difficult to simulate by technology (Autor et al., 2013) and are characterized as human abilities. Emotional communication and empathic behaviour are irreplaceable at the workplace (2#12, 1#26). The human touch (2#11), as well as emotional intelligence, are helpful at the workplace to encourage better cooperation between employees and to build a more satisfying working environment in general. Furthermore, social and emotional intelligence has always been a core competence of people in collaboration and building trust-worthy relationships (1#18). From a leadership perspective, empathic interaction helps to motivate employees (1#26). As AI becomes increasingly sophisticated and human-like, but is still difficult for technology to adapt, employees must be able to have the right set of soft skills and perform well in an emotional world and work environment. Through change management modules and training sessions, employees can be trained on how to interact with machines, but most importantly with their colleagues (1#26).
Interestingly, the research shows that experts, depending on their professional background, either advocate a more human or a more machine-based future work approach. This means that in a technology-oriented workplace, the machine is more in the focus than the human (1#5). Contrary, however, for teamwork tasks it is stated that a real face-to-face conversation is more valuable than a virtual conversation (1#22).
Working in a targeted manner with machines (both as human-computer and human-robot collaboration), interaction is becoming increasingly important, enabling an effective cooperation and the controllability of machines. Before that, a decision has to be made which activity a human can overtake and where the humans should be supported by machines. Work can then be delegated.
Experts agree that repetitive tasks can be automated (3#3,1#5, 1#7, 3#10). Interestingly, business
78 models where the human being is performing repetitive tasks are supported by experts, and it is accepted that these tasks can be taken over by robotics and technology.
AI will have a lasting impact on knowledge work but will not displace human judgement. It is not a question of playing machines off against people, but of how the two can work together meaningfully.
The combination of peoples’ relative strengths with the strengths of machines leads to the best kind of AI-supported decision-making. Algorithms find solutions to problems much faster than humans, however often not with sufficient sensitivity, intuition and prudence. HR experts state the example in recruiting: video applications reviewed by robots are tested, as the first step in recruiting processes could be automated. But the real face-to-face interview and selection of the right candidate should not be automated according to the experts, as gut feeling and human interaction play an important role in the decision-making process (2#14, 2#16).
In the future, working with automation and adding human value will be a typical scenario. In these regards, it depends on the industry how much automation will exist. Many experts from Group 1 state that in their industry, automation of specific tasks will be inevitable. The workforce of the future will be working with machines and technology and using their advantages for daily work routines. In this transformation, it should not be discussed whether to use automation or humans, a symbiosis between them has to be achieved. Having a basic understanding of data literacy, combined with a soft skillset and of how to use human-machine interaction will be a strategic advantage in the future work environment. One expert introduces the “humachine” term, which calls for a combination and symbiosis between humans and machines. In Figure 12 the main findings - the categorization of human abilities in social interaction, cognition and creativity and the technological superiority in automation of repetitive tasks, power & quality as well as decision-making are summarized.
Figure 12 Humachine, after Daughtery & Wilson, 2018