First of all, across all expert groups it can be noticed across all expert groups that many experts
perceive soft skills or “21
st
century skills” as human skills (1#1, 3#10, 5#4, 1#7). This is interesting
because future skills have in the eyes of many experts the connotation of being human-driven. This
is in line with literature, as MacCrory et al. (2014) investigated the skills that machines (thus far) have
not yet been able to adopt for example in interpersonal skills. Machines can complement or take
71 over many aspects of life. But in their social interaction, their empathy, their emotions and their ability to listen actively, they are still inferior to humans.
In the literature, human skills are generally seen equal with social skills (Deming et al., 2017; Autor, 2015; Borghans et al., 2014; MacCrory et al., 2014). The findings in the present thesis offer perspectives from different backgrounds. Interviewing experts about their opinion on necessary human abilities revealed a large variance of possible future skills. Those with a technology background consider digital skills as most important: three experts with technology, HR and education background highlight this (2#16, 1#17, 5#19). However, experts from consulting or academia advocate human skills, or so-called soft skills, more rigorously.
On the basis of three examples, the differences between the diverse expert groups are highlighted.
5.1.1 Creativity – Major Difference between Humans and Machines
Literature mostly portrays creativity as an artistic process and the creation of the “new” (Frey &
Osborne, 2013, Brynjolfsson & McAfee, 2016). The empirical results in this thesis found the formation of creative thinking to be essential for the activation of human abilities (3#6, 1#7, 3#9).
This was expressed frequently in the interview setting throughout all expert groups. As the experts state, creativity, in form of innovation, originality and lateral thinking is seen as a human ability.
Even though technology can already build up creative processes, as mentioned by one expert (5#4), the question has to be asked if it is the same creativity that humans would exhibit (1#19). Of course, technology can reproduce pictures and even paint them with a brush, but they don’t know why they are doing it (5#4). Economists are certain that creativity is a human advantage over machines (Brynjolfsson and McAfee, 2016).
Robots usually lack creative qualities, with some exceptions in AI (1#24). Thus, creativity is seen as a process that should be still in human’s hand, because the “courage to think differently” is exactly what makes humans unique. Creativity requires further risk taking (5#19). This also means getting involved in decisions that initially seem wrong. It is hardly possible to program a computer with this kind of intuitive, creative thinking (1#25).
Interestingly, creative processes have been mentioned by experts with technology background, who
state creativity as the process of creating something new. This is a prerequisite for developing new
72 technologies in unknown environments (1#18, 1#21, 1#24). Academia is certain about the importance of new innovations (3#6, 3#9), and consulting experts highlight the potential of designing creative work process areas (4#2, 4#8). Companies have to strengthen the creative mind-set, which can also go along with a new office interior concept. HR managers encourage companies to strengthen the creative design thinking in daily business (2#12, 2#16). Design thinking, service design and other innovation methods are highly valued by managers (2#12, 2#16). But especially in automation, creativity plays an important role as a unique selling point. While machines are built to solve problems efficiently and logically, they are not yet able to produce truly original and creative content (1#5). Having a creative mind-set and extrapolating from a known and well-defined state into something that hasn’t been exposed before (1#18), will not only in the manufacturing set-up be relevant, but also in any other business, as well (1#18, 3#13).
5.1.2 Empathy and Emotional Intelligence – Roadblocks for Automation?
The mechanisms of human interaction and social cooperation, as well as emotional intelligence should according to different experts (5#4, 4#8, 1#24, 2#11, 2#12, 2#16, 3#10, 4#8, 5#19) not be automated. This is in line with the literature and arguments presented by Autor, Premack & Woodruff, Baron-Cohen and Camerer et al. (2005). But in difference to the literature is that these authors describe a generalized view of the debate.
The empathetic behaviour was mentioned by experts from different backgrounds as an important ability. Interestingly, those who demonstrate a more technical mind-set according to their background highlight the empathic behaviour as a main human ability. They affirm that empathy and social interaction cannot be automated by robotics. What machines are still missing is to link emotions with the environment (1#7). Therefore, in a technical environment empathy needs to be strengthened and is seen as crucial to support human interaction on an emotional level. One expert from Group 1 brings up the example that empathic and human behaviour is in a social context environment, for example in a hospital, probably more important than working in a factory and producing a car (1#5).
But the same expert is forecasting that emotional interactions can never be fully replaced by technology (1#5) and therefore, the human being is irreplaceable in a technical surrounding.
It is interesting that experts from the manufacturing and logistics industry state that empathy is a
human ability, because it could have been expected that this ability is not much needed in this kind
of work environment (5#19, 1#24). But it is clear that for instance in logistics, only tasks to some
degree can be automated, therefore the human is still essential in carrying out certain tasks (1#17).
73 In a new leadership style, emotions and empathy are needed, also in the manufacturing and technology industry (1#24). From the HR perspective, empathy is a prerequisite skill not only for an employee, but also for a client-driven organization itself (2#16).
The discussion about empathy and emotions in the technological industry is interesting to register.
As many processes in the manufacturing industry have been or are automated, the scenario of a machine-driven workplace has become the reality (1#5). In the end, the human’s empathy and emotions in interactions with each other are also important at the factory workplace. This could be an explanation why experts with a technical background classify emotional intelligence for their kind of workplace as relevant: They are the ones who are most familiar with capabilities of robots and know the limitations of automation well. Keeping this in mind, a new training concept for manufacturing with regard to soft skills training should be implemented.
As technology gains importance in more and more industries, there is a need for people to guide others in the use of these new technologies. Pure technological expertise alone does not suffice anymore (1#5, #1#17). As technology becomes more sophisticated, “empathic nerds” (2#14) are needed who can communicate not only with each other, but also with the customer. As tasks change, jobs are redefined. At the same time, companies will have to become more agile, which aims to ensure that companies and parts of companies remain adaptable and constantly reinventing themselves (1#24).
5.1.3 The Human Brain – Distinct Trait of Humans
Highlighted by four different experts from the technology industry, HR, academia and education, human intelligence with all its different manifestations is identified as a unique human ability.
According to them, human intelligence cannot be automated, as the human brain is too complex to be taken over by robotics (2#16). The human brain consists of different spheres and has a processing speed that technology cannot handle in this construct (5#20, 1#25). This highlights the inherent complexity human intelligence masters, and which would have to be performed by an equally intelligent machine. Therefore, it is highly disputable, at what time in the future a machine will achieve cognitive intelligence or whether it will be possible at all (1#25).
In order to be able to imitate human emotions entirely, more knowledge about the functions of the
brain would be necessary, for example to recognize which information is relevant at all (1#25). Frey
74 and Osborne do not expect this problem to be solved by engineers in the coming decades (Frey and Osborne, 2013).
This is related to intuition or gut feeling, because intuitive mechanisms are controlled by the human brain. In decision-making, data can help, as agreed by some experts (5#19, 3#13, 2#16), but in the end, intuitive decision-making by a human being is classified as more important (5#19, 1#23).
Machine intelligence complements the human intellect but cannot replace it in decisions requiring emotional and social intelligence.
5.1.4 Superiority of Technology – a Fact to Accept by Humans
Furthermore, the findings suggest that a superiority of technology has to be accepted in areas where humans cannot play out their human advantages. Also, expert groups that are not working in logistics, manufacturing or with AI technology agree to the superiority of machines in routine or administrative tasks. There is however not a rejection of the technology itself; on the contrary, for the automation of monotone or boring tasks, the use of technology is appreciated. Ideally, machines should take over activities that are repetitive or associated with particular health hazards. On the other hand, they should complement human decisions where their specific abilities are able to recognize complex interrelations better than a human being, e.g. in medical diagnostics.
Autor et al. (2003) accept superiority of technology from a generalist point of view, but they don’t ask where this perspective is coming from. According to the experts with a technology background, the superiority of technology is obvious, as humans should concentrate on their human skills (1#24, 1#18).
Besides the obvious acceptance of technology by experts from the technology industry, it is
interesting to find the same acceptance by other experts. HR experts claim that everything that can
be standardized should be standardized (2#12). Inefficient processes (2#16), as well as speedy
calculations can be directly automated (2#14). Academia is finding advantages of technology in the
following procedures: in decision-making (3#6), information filtering (3#10), as well as predictions of
several scenarios where technology, like AI, is quicker and more precise (3#9). With Machine
Learning, it is easier to create new knowledge or new images (3#3), and the combination ‘AI +
human’ is emphasized. For the educational sector, it is advantageous that algorithms are better in
speed of data processing and memorization (5#13). Due to their 24-hour energy level, algorithms
are faster and quicker in processing and analysing (5#4).
