How to Save Human Jobs?

Automation of jobs is slowly but surely impacting human capital. A combined obligatory action from human capital by remaining vigilant and adapting to lifelong learning; organizations by setting aside human capital development fund for upskilling and reskilling the human capital; and government by taking legal actions to ensure fair and transparent employment practice is more imperative than ever. Perhaps if we compare the scale of worldwide unemployment caused by automation and ignited by Covid-19 pandemic restrictions, saving jobs of human capital may sound like wishful thinking. Based on research done by organizations such as McKinsey, World Economic Forum as well as articles that have been published in the top-notch journals, newspapers, and magazines, in the past few years and especially now, during the pandemic, human capital is on the cusp of being resilient and agile.

In the U.S., like in many other developed nations, workers, government officials, and other stakeholders have been vocal about automation replacing jobs by completing their jobs faster, with fewer errors, and in a fraction of time. Five years ago in 2017, a new rule of increased bonus depreciation to 100% was introduced in the Tax Cuts and Jobs Act which allows organizations to write off the cost of property during the first year of its use. While the idea of bonus depreciation is to incentivize organizations to create a positive effect in the labor market by hiring more workers or increasing wages, for example, no rule restricts organizations from purchasing machines that can automate works and displace human capital.

Thus, based on research, in some cases, bonus depreciation has been counterintuitive. It is because some organizations that use the bonus depreciation incentive, purchase machinery and equipment that automate human jobs turn out to be more efficient and profitable but such organizations do not have the compulsion to re-skill their employees or create more jobs. As a result, organizations are not creating as many jobs as they are profiting. Comparing employment trajectories of the most profitable companies a century back and now, it is clear that organizations in the 1900s like General Motors, for example, needed more human capital hence created more employment than organizations like Amazon for example in the 21st century and in retrospect, organizations in the 21st century more profitable. Thus, the increasing number of automation, the implication on tax law in employment, and increasing affordability of automation boil down to the upward trajectory of the changes in jobs resulting in human capital being on the verge of being replaced by automation. As human capital is one of the most expensive assets of any organization, their development and upkeeping is dependent on a legal framework that addresses concerns brought by automation, organizations who provide employment, and human capital themselves.

From a historic point of view, this is not the first time that the fear of jobs being automated is being felt. During 1800 when Jenny the weaving machine automated jobs cloth weavers resulting in Luddite riots. As it is believed that history repeats itself, the reaction of truck drivers with a strike at the Port of Los Angeles, California is a replication of Luddite riots as automated loading dock trucks were introduced to lower the cost of the company and also reduce pollution as the company claimed.

On the contrary, since the Luddite riots of the 1800s, the total number of jobs for human capital has increased. The jobs that were done back then which were more agri-based, manual, and repetitive have now been taken over by machines. Human jobs have become more knowledge-oriented, analytical, and cognitive. The classic example that can help define this conundrum is the introduction of ATMs by the banks. When the ATMs were introduced in the early 1970s, several teller jobs fell but the number of bank managers increased over time as banks could shift the funds that were previously used for bank tellers towards increasing positions for the managers. This shows that the nature of repetitive jobs changed from counting and disbursing money which could be done by the machine efficiently and accurately and shifted towards customer service which is more challenging for the machine to replicate. And now again, the number of bank branches and the relevant jobs have decreased are replaced by online banking where more sophisticated and uses advanced technologies.

Another example is Microsoft partnering with the U.S. Department of Agriculture and farmers to use data of the crops, planting moisture in air, weather, and artificial intelligence to produce crops that are climate resilient which in the past used to be done by scientists in a silo. This also means that the jobs that took many scientists to perform and collaborate as they were collecting data in a silo in the past could be done by AI. Similarly, another example is amazon using bots in its warehouse to sort items and bring the correct ones based on the order received. Similarly, the taxi drivers protested when Uber came up with a different business model which threatened the way ride-hailing businesses operate. And should now the automated cars should find solutions to conundrum such as the ‘Molly problem’, the jobs of taxi drivers are on the verge of being automated completely.

As a result of the COVID-19 pandemic, machines that prepare salad, checking the temperature of people at the airport, and automated dogs are reminding people to keep 6 feet distance if they are closer, and tracking people via mobile apps in case they have been to an area where they could have been exposed to a person with the Covid-19 disease have been launched. Similarly, online deliveries of food and other everyday essentials skyrocketed using online application software. If the trend continues even after the pandemic is over, then the jobs that were previously done by humans will be done by machines and the supply chain management has to be rethought. And the fact that the capability of the machine has been developed and as machines are becoming more affordable, the uptake of technologies in the everyday life of humans affecting jobs of human capital will only increase.

Human capital: All these examples only mean that the pattern of employment keeps on changing and the pace of change is getting faster. Thus, it is healthier for human capital to vigilant and upkeep their personal growth, reskill themselves continuously, and adapt to lifelong learning. Having the perspective of learning as a lifelong task rather than achieving a degree or completing a job will help human capital brace through any challenges that automation may bring to the workplace. And individuals need to have their minds open to changes and be agile. As automation may change a task of any job, people in all disciplines need to be ready to adapt to any type of change. Having said that for some jobs, the degree of automation will be in a higher degree, and as a result, the job will become obsolete and the human capital will need to look for another job in a different field. And in some cases, like in the case of ATMs taking jobs of bank tellers, more bank manager jobs that cannot be easily automated will emerge for which the human capital will need to be ready and capable of handling. And in some cases, like in the case of the taxi drivers where the jobs are being completely automated, the human capital will need to look for jobs of a different nature.

Organization: As an adaptation of automation by organizations will result in redundancy of human capital jobs, organizations need to take a stake in conscientiously reskilling and upskilling existing and incoming human capital. This idea of lifelong learning also needs to be supported by the organizations by setting aside some human capital development funds from any surplus from an adaptation of automation to train the existing employees and having an outreach at the universities and schools to bring awareness amongst future pillars of human capital.

Adaptation of automation in the workplace many times simplifies human jobs such as automating repetitive and dangerous tasks of a job, it is considered good but sometimes, automation of a major task or many tasks of a job also may result in unemployment of the human capital. It is worthy to note that while the nature of jobs has been changing due to some level of automation of jobs since the 1800s and trend has been increasing since then, the employment protection legislation around the world has not evolved as fast and is trying to keep up with the changes in the employment pattern brought by automation. Similarly, organizations need to closely monitor any trends of creating unemployment or discrimination of race or gender because of the use of automation and take actions to overcome such negative consequences.

Legal: Studies show that given that automation such as machine learning heavily relies on past data, the results from automation have a bias towards women and people of color. This means that if not implemented and monitored by employment legislation properly, automation will exacerbate already existing disparity against people of color and different genders. On the contrary to the employment legislation, like mentioned earlier, amendment of tax law in the U.S. is encouraging big firms to use advanced technology. This may not have been a problem if technologies were not evolving as fast and are becoming more affordable. And in addition, the incentive that the companies receive is good for the companies and the shareholders but not for the human capital as they become unprotected and dispensable as more automation is adopted. If a proper legal framework to set aside human capital development funds to develop diverse human capital in an inclusive environment is mandated, then organizations will be bound to take the development of human capital as a priority.

Legal transformation of employment law geared towards protecting human jobs will make organizations obligatory to compensate the pool of workforce displaced by adaptation of automation. For example, any funds raised or profit from the organization profiting from automation, but displaced workers can be used to reskill them. Similar to how organizations set aside research and development funds for a new product, funds can be allocated to train the employees who need re-skilling. If all the organizations are mandated to set aside these funds then the existing employees will be of high quality and the replacement of any turnover will be of high quality as too.

A combination of a strong foundation with proper employment law that mandates human capital development as organizations adopt automation at the workplace, followed by the organization using reskilling and upskilling of existing and incoming human capital at all levels, and finally human capital themselves taking the effort to upkeep themselves continuously and adapt to lifelong learning will be the savior of human capital from the verge of being unemployed in the era of automation as it becomes more affordable.