Automation is already being used across all sectors and in some ways it is forcing society to reassess the role people play in industry.

For example, if artificial intelligence systems are better at diagnosing disease, what is then the role of doctor? An educated guess would be that their role would be to spend more time face to face with each patient or perhaps developing better patient care strategies.


Machine learning is also taking over repetitive tasks (data entry or contract drafting) alleviating the workforce from this type work could open an opportunity to focus staff on tasks that require higher cognitive attributes, such as negotiation, collaboration, or idea generation.

The shift in the workforce is already happening, in a recent report from McKinsey analysing over 3,000 people in over seven countries points to the following findings.

  1. Basic cognitive skills, which include basic data input and processing, will decline by 15 percent.

  2. Basic cognitive and manual skills will decline.

  3. Digital requirements of most jobs has increased.

  4. In aggregate, between 2016 and 2030, demand for these social and emotional skills will grow across all industries by 22 percent in Europe.

  5. Academic research has shown that non-routine interpersonal and analytical tasks in occupations have been rising over the past 50 years, even as routine manual and cognitive tasks have declined.

The Rise of A New Workforce

From finance, to insurance, to creative agencies, industries across the board are evolving their talent pools. Whilst it is true that companies have always wanted the best talent, automation has elevated the stakes. Companies are beginning to understand that, in order to stay competitive, they will have do more than just invest in the best technology; they will have to invest in hiring, training, and honing a new type of human capital.

The-higher level tasks that companies are now looking for are creativity, complex information processing and interpretation, interpersonal skills and empathy, advanced communication and negotiation skills, adaptability and continuous learning. Whilst the computation capabilities of AI are impressive and will undoubtedly catapult problem solving capabilities to a new era, there will still be a significant need for human intellect.

Accordingly, the aforementioned McKinsey study also found that social and emotional skills will grow rapidly - skills that are necessary for machinery, but very much needed to conduct business. According the McKinsey’s analysis social and emotional skills is expected to remain a feature of the economy in 2030 in the UK.

These skills accounted for more than 21% of the working hours across the economy in 2016 compared with 18% in the United States, and it is estimated that this proportion will rise to 26% in 2030 versus 21% in the United States.

Here is a synopsis of 4 key cognitive qualities that will most be needed in 21st Century work



Working memory can be defined as the short-term maintenance and manipulation of information necessary for performing complex cognitive tasks such as learning, reasoning, and comprehension.

According to their multicomponent conceptualisation, working memory comprises a phonological loop for temporarily manipulating and storing speech-based information and a visuospatial sketchpad that performs a similar function for visual and spatial information. Both are supervised by a limited capacity central executive, a control system responsible for the distribution of attention and general coordination of ongoing processes.

A fourth component, the episodic buffer, was added to the model in 2000; it binds together information about the same stimulus or event from the different subsidiary systems to form an integrated representation that is essential to long-term memory storage.



Cognitive flexibility is a cognitive process of executive function by which previously learned behavioural strategies can be modified to adapt to changes in environmental contingencies. Enables adaptation to new situations by switching from previously held beliefs or thoughts to new response strategies (Anacker & Hen, 2017).

For example, if you learn that you have a new deadline, with the use of cognitive flexibility you would employ a new strategy to execute the new deadline. You might call on other colleagues, find the most efficient manner to fulfill the deadline, or use available tools. The contrary to this would be to dwell on the discomfort of the new deadline and have no adaptation.



There are two main strands to definitions of empathy. The first refers to the mental perspective taking, which is cognitive empathy. You can see the world beyond your sense of self as well as understand the effect of your behaviour. The second is the vicarious sharing of emotion, which is emotional empathy.

Facets of Empathy:

1. Theory of mind (ToM) is an important skill that refers broadly to the capacity to understand the mental states of others. ToM can be split into affective and cognitive ToM. Whereas “hot” or affective ToM requires an understanding of others’ emotions or feelings, “cold” or cognitive ToM requires an understanding of their beliefs, thoughts or intentions

2. Deficits in reasoning about another’s beliefs, feelings, desires,intentions or goals have clear and important consequences for a number of clinical groups, and can profoundly limit functional capacity and quality of life. Across normal human development, individual differences in ToM also have important implications for social competency.



As described by (Kidd & Hayden, 2015), one factor that has hindered the development of a formal study of curiosity is the lack of a single widely accepted definition of the term. In particular, many observers think that curiosity is a special type of the broader category of information-seeking, but carving out a formal distinction between curiosity and information-seeking has proven difficult.

Consequently, much research that is directly relevant to the problem of curiosity does not use the term curiosity and, instead, focuses on what are considered to be distinct phenomena (e.g. play, exploration, latent learning). Conversely, studies that do use the term curiosity range quite broadly in topic area. In laboratory studies, the term curiosity itself is broad enough to encompass both the desire for answers to trivia questions and the strategic deployment of gaze in free viewing.

Kidd & Hayden (2015) consider this diversity of definitions to be both characteristic of a nascent field. They describe the characteristics of curiosity as follows: In the domain of function, it seems clear that curiosity serves to motivate acquisition of knowledge and learning. In the domain of evolution, it seems that curiosity can tentatively be said to improve performance, yielding fitness benefits to organisms with it, and is likely to be an evolved trait. In the domain of mechanisms, it seems that the drive for information augments internal representations of value, thus biasing decision-makers towards informative options and actions.

It also seems that curiosity activates learning systems in the brain. In the domain of development, we can infer that curiosity is critical for learning and that it reflects both external features and internal representations of own knowledge.

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Josh Artus