Labor can be defined as “the amount of physical, mental and social effort used to produce goods and services in our economy” (Amadeo, 2019). According to Warner (2005), the idea of physical and mental labor being separate is untrue and that there is often an overlap of characteristics. With modern day advancements in information technology, the mental process of labor can be transformed into machine process which has proven to have major consequences on the way we live and work today. Mental labor can be distinguished further into two subgroups: ‘universal labor’ and ‘communal labor’ (Warner, 2005). Universal labor is concerned more with the process of mechanizing mental labor by creating the machinery to do so, whilst communal mental labor is to do with the operation of such machinery.
In relation to the theories proposed by Warner (2010), we see that human mental labor can be categorized as ‘semantic labor’ and ‘syntactic labor’. Semantic labor covers the idea of human perception and interpretation of their knowledge, for example, the writing of an essay would be described as a semantically driven piece of mental labor in comparison with syntactic labor, which is aligned more with the processing of patterns, facts and figures for example a mathematical equation. These ideas are paramount to the understanding of the mechanization of the human mental capacity. Hern and Milmo (2015) argue that machines are much better at acting syntactically as opposed to semantically and use the nursing profession as an analogy, saying, “A career like nursing ... is the perfect mixture of almost everything a machine finds hard: fine motor skills, specialist knowledge, a wide variety of potential complications”. This analogy suggests that machines do no react well to ever changing environments and is further supported by Searle (1980), who suggested that computer programs “do not provide sufficient conditions of understanding” unless there is a direct correlation or pattern with the data it is given. An example of where syntactic process is useful would be in a large warehouse, e.g., an Amazon store, where all items are given an exact code and this information is stored on an RFID tag, which can easily be scanned in and out of the warehouse. In this case, lots of information including price, location and stock can be determined from the product code which helps staff in the warehouse to process orders. Each member of staff is given a handheld device which prompts them with the orders they must prepare, and the device instructs them on where to find each item. Notifications are tailored to the area of the warehouse the staff are working in and therefore this majorly boosts productivity in comparison to the task being completed humanly (Baraniuk, 2015).
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In today’s society it is nigh-on-impossible to get through the day without encountering an example of a process which has changed from being a human clerical role to an automated equivalent. Perhaps one of the most common and well-established examples of this would be the automated teller machine (ATM), which was first introduced in the 1960s (Batiz-Lazo, 2015). Prior to the diffusion of the ATM machine, patrons would have had little other option than to go into a bank to collect or deposit cash, a request would have been made to the bank clerk by giving their bank account number and a form of ID. The bank clerk would then have made a syntactic decision based on the amount of money in the person’s account on whether or not to give out the money. With the ATM machine a person could go to any machine, at any time of the day to collect their money, the machine would make a syntactic decision based on the amount of money in the account which was accessed using a card with a magnetic strip or chip holding account details and required a 4-digit pin to be entered to open it. Despite their relative infancy, it is already becoming clear that this process too is becoming the subject of further mechanization in relation to the roll out of contactless payments, in 2019 alone there was an increase of 88 million contactless payments in the UK between June 2018 and June 2019 (Cherowbrier, 2019), which would indicate a move toward less cash being collected in our society. The rise in contactless payments demonstrates a real increase in productivity in terms of the time taken for transactions to take place. Warner (2010) argues that the replacement of the direct human labor element will in turn speed up the process and therefore be more attractive to consumers.
Through the process of diffusion there has been a significant change in working practices throughout the 21st century. These changes are most noticeable in terms of a switch from manual semantic processes, which require human clerical labor, to machine syntactic processes, which enable the mechanization of industry. As a result of the switch to mechanization the costs associated with maintaining an expensive human workforce have been decreased, Wiener (1954) describes the impact of machine process of being “the direct equivalent of slave labor” and it therefore accepts the same economic conditions of a slave making it a more attractive option to business owners. Carr (2015) further emphasizes this point by stating that “if a robot could work faster, cheaper, or better than its human counterpart, the robot would get the job”, evidence of the cost benefits of a robot are shown in the automobile industry where, on average, a German car worker will earn €34 per hour in comparison with a robot with an operating cost of around €8 per hour (Bowcott, 2017).
Human attitudes towards the mechanization of labor have changed through time to a point where humans now fear that they may lose their jobs to a robot. Bowcott (2017) reported that due to the rise in artificial intelligence and robotics we could soon see a need for “governments to legislate for quotas of human workers” to stop the complete overhaul of the human workforce. Ellingrud (2018) disputes this viewpoint however and claims that despite a perceived replacement of humans, jobs in practices prone to automation such as manufacturing, have actually grown at their quickest rate since 1995. Mahdawi (2017) describes this idea as ‘luddite fallacy’, in reference to a 19th century group of textile workers who destroyed the machinery which rendered their skills redundant. Warner (2010) argues that the increase in machines in the labor force is a positive factor and that they can “speed up processes and enable previously impossible activities”, evidence of this has been found in the medical field with the development of the ‘Da Vinci robot’ in 2000 which is capable of carrying out procedures which to this day are unable to be performed by human hands due to their complexity. Between 2000 and 2014 over two million medical operations had been carried out by these machines citing a revolutionary change in how medicine was and still is practiced (Piesing, 2014).
As discussed, there are only certain forms of labor which are suitable to be transformed into and information technology process. Semantic forms of labor are, as described by Macpherson & Kontos (1979), ‘distinctively human’ and therefore require direct human engagement with their respective processes and are therefore more difficult to compute, in comparison to this, syntactic processes are much better suited to machine computation due to their reliance on data and patterns (Warner, 2010). According to Wasen (2015), it is ‘repetitive work’, which is much more likely to be replaced computationally, which would suggest that roles within manufacturing and agriculture are the most likely to become automated. In terms of my own experiences in the working world I have seen a change in the baking industry where packaging is now completed using a robotic machine as opposed to being packaged by hand as was the case at the start of my tenure there. I was fortunate enough to be placed in a different area of the bakery, however some were not and lost their jobs as a result. This is a clear example of creative destruction within the business and indeed our economy. The robotic machine is able to create the tasks more quickly and at less cost, however people have lost their jobs as a result which holds an ethical implication over its use.
Syntactic processes are often transferred to become machine processes in order to compete in larger and ever-changing marketplace. According to Samii & Karush (2004) information technology has become a ‘strategic tool’ within any large business, and therefore through its use companies can make significant gains on their competitors by utilizing it effectively, i.e., speeding up processes, reducing costs and reducing the impact of human error. It is generally the roles of middle management roles which are under threat to information technology processes in this respect and according to Elliot (2019) there has been a perceived hollowing out of the middle classes. There are however occupations which have so far resisted the transformation to automation due to the demands of their role. Roles which are most likely to resist automation are those which require “genuine creativity, such as being an artist, being a scientist, developing a new business strategy” (Ford, 2017) due to the computer’s inability at this moment in time to be creative, to think on the spot and react to ever changing situations, all of which are important in the aforementioned types of occupations. Marr (2018) writes that jobs requiring ‘soft skills’ such as communication, empathy, creativity, strategic thinking, questioning, and dreaming are those which are at least risk, but stresses that they will become ‘hard currency’ within the job market with the advancements in automated intelligence. When I undergone my placement at Fujitsu UK, I was deployed within the service management department and was able to see the benefit of both automation and of the personal human touch too. In particular, jobs which required face to face interaction with the customer would have been very difficult to automate and had they been so would have caused disruption and affected customer satisfaction. For example, the customer wanted to place an order for new hardware and were pricing several different suppliers and therefore bargaining techniques had to be used on each side to thrash out a deal, this process would have been very difficult to automate and therefore resistant to transfer to information technology.
Management of resources is key to the success of any business and this is no different in terms of how labor is distributed between human semantic work and information technology machine process. The distribution of tasks depends on the type of labor required for the task in hand, for example a role in an office which requires an employee to think and interpret data and respond to customer requests will require intellectual labor, whereas another employee may only be tasked with a more clerical task such as updating a payment spreadsheet. Warner (2010) describes intellectual labor as being “deeply rooted in human understanding” and therefore this type of work demands a higher renumeration package as opposed to an employee carrying out the clerical tsk which requires much less understanding and experience. Due to the differentiation between these roles in terms of salaries and depending on the needs of the business there are normally less employees carrying out intellectual labor tasks in comparison with those carrying out clerical tasks. In terms of the distribution of labor at my placement in Fujitsu there was a clear hierarchy of positions and the number of people within each reflected this. At the bottom of the ‘food chain’ there was the Service Desk, whom provided first line support to users within our infrastructure, these members of staff were paid the least and there was a large turnover of staff members. The work carried out by the team was often automated by the service desk system that was being used to suggest solutions based on what the user was describing on the phone. If the system did no automatically suggest a solution, they could type the symptoms into a large knowledge base and a solution would normally be returned.
In contrast to the Service Desk, there were only two solutions architects, both with over 20 years of experience within the company, their knowledge and experience is more difficult to come by given the skill level and training required to do it and therefore they were entitled to a greater salary. Due to the bespoke nature of the requests that are made by the customer to them there is very little in terms of automation within this role hence the reliance on knowledge and experience. As acknowledged earlier computers are not as useful when it comes to adapting to changing environments, and therefore their automated input is much more useful for the service desk who dealt with similar requests every day such as resetting a user’s password. It is vital that managers utilize information technology efficiently throughout a business to give a ‘competitive advantage’ with their ‘value chain’ (Porter & Miller, 1985) over competitors, however, it is not viable to transfer all work to information technology.
Conclusion
It is clear to see that throughout the last 20 years there has been a shift towards the mechanization of mental labor to the extent that there are jobs which have become extinct and also jobs and industries have been created by it. Through the fast-paced evolution of working processes industries are now able to perform tasks much more efficiently and effectively than in comparison with their previous clerical processes.
There are certain jobs which have been fortunate enough (in the worker’s sense) to avoid automation at this point but there is evidence to suggest that computers are evolving much faster than humans (Sahota, 2018) and this may not be the case for some jobs in the future. The jobs most likely to avoid this though are those jobs which require human intuition and creativity to be successful such as the role of an architect designing bespoke housing (Davis, 2015).
Jobs such as the role of the cashier have not been so fortunate in terms of their likelihood to be automated. Every day we walk into a major supermarket such as Tesco or ASDA we come across self-service checkouts which in some stores can eliminate up to six jobs. We see further evidence of this at airports now with the rise in self-service checking in for flights, although this is much more convenient for consumers.
The changes that have been witnessed as a result of the mechanization of mental labor in terms of working practices can be seen as having had a positive impact overall. Organizations such as Fujitsu, where I worked at, must understand that these innovations will never stop and the only way to keep up with the world around us is to move forward with it. Organizations must not see the evolution of automation as a threat to its personnel, but rather as an opportunity for growth and to champion the idea of creative destruction within the labor market. With the help of computers, we can expect “greater GDP, higher productivity and increased customization of the consumer experience” (Pettinger, 2019), this does not sound like ‘doom and gloom’ to me.
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