A brief overview of technological disruption on industries
The automation of activities via technological breakthrough is no novelty to society. It no longer strikes us as surprising the fact that pilots only steer the plane themselves for around 10% of the course, or that money is drawn from ATMs rather than from a bank teller, as opposed to what happened a few decades ago. However, as we experience AI and machine learning development at an ever-accelerated pace, the future of many industries and employment as we know it may be at stake.
Overall, economies had to adapt to maximise the value they get from the digital disruption phenomenon, but at a micro level how did businesses across industries changed? First, they transferred some of their power to consumers, making their needs the main focus of the company. Also, they changed the way they operated, shifting to a more agile and sharp way of acting, simplifying the decision-making process and making their dynamics more competitive. And lastly, firms reinvented their operating models, using advanced analytical tools in order to reduce costs and to drive revenue, while improving insights.
Recently, AI has been subject to groundbreaking discoveries, accounting for significant advances in many sectors and placing us in the middle of the fourth industrial revolution. As the world’s top enterprises strive for the best AI in order to capture its vast market (Amazon with Alexa, Apple with Siri, IBM with Watson, and countless other examples), we observe time and again a wider scope of industries that are possible to restructure and enhance via technology – healthcare, retail, manufacturing, finance, customer service and transportation, only to name a few.
As more companies adopt artificial intelligence for revenue boost and cost reduction, global AI startup funding has been growing vertiginously over the past years. The ease with which manipulation of AI capabilities can be done allows each industry to tailor it to their value chains and, consequently, increase efficiency.
What’s more, machine learning is also a tool increasingly more explored by corporations, from financial services to entertainment. PayPal, for instance, makes use of machine learning to analyse and compare users’ activity in order to detect legitimate and fraudulent transactions, namely money laundering. Netflix makes use of intelligent machine learning algorithms that compare viewing activity in order to make recommendations on what to binge-watch next. These sorts of tasks involve the quick analysis of big data, a task in which humans cannot compete with machines.
One of the sectors that is already being revolutionized by technology is transportation, and one does not even have to go as far as Tesla to mention automated vehicles. As Mobicascais rolls-out its first automated bus, the future of self-sufficient public transportation is imminent. And it does not stop here: with multiple trials on autonomous ships, mining trucks and aircrafts, the only thing stopping driverless vehicles from becoming mainstream is regulation.
When it comes to manufacturing, the automotive sector is historically known for using cutting-edge technology to improve efficiency. While Industry 4.0 technology brought considerable gains in productivity, ranging from production design to quality control, being able to keep up with increasing consumer demands for more choice, it has also been able to mitigate the fear that this industry belongs to the robots. Breakthroughs in the field of robotic technology brought the so-called “co-bots”, artificial intelligent robots that are much lighter and agile – thus safer for humans to work around – and, more importantly, they are trained rather than programmed. A study conducted by the MIT in alliance with BMW found robot-human teams to be 85% more productive than either of them alone, and the manufacturing industry is responding, tending towards a common-ground future for man and machine.
By affecting industries, automation is bound to change labour. In fact, this has been a trend for decades now and the further development of AI will inevitably change the workplace and how we work, which will bring positive and negative consequences. The global impact, however, is still unknown.
In the US, since the 80s, computers have led to 3.5 million jobs destroyed, according to a McKinsey study. Nevertheless, in that same time frame, over 19 million jobs were created as a result of the personal computer, as technology increased productivity and spending power, which consequently created new demand and new jobs. Technology has changed labour, not destroyed it. As the service sector gained weight, so did the median household income, as a result of some low-paying and low-skill jobs being replaced. Also, automation led to vast improvements in terms of quality of life, hours of work as well as replacing repetitive tasks, meaning its impact extends beyond just economic indicators.
AI, machine learning and other recent technologies stand to change the labour market similarly to how computers did, although to a further degree. The World Economic Forum claims that from 2018 to 2022 automation will destroy 75 million jobs, but, as was seen previously, 133 million jobs will emerge in that period due to the same event. However, when talking of automation and job destruction, it is important to distinguish between occupations and activities. According to McKinsey, 45% of activities performed can be automated by adapting currently demonstrated technologies. When it comes to occupations that may be fully automated that figure is only 5%. This scenario is far less drastic, because the change affects primarily tasks rather than occupations themselves. Nonetheless, 60% of occupations could have 30% or more of their constituent activities automated, which means the vast majority of professions will still suffer significant changes, namely job redefinition along with transformation of business processes.
The difference from previous seen automation lies in the incidence. Whereas the industrial revolution led to mainly low-skill tasks disappearing and the computer age affected workers more in the skilled middle, such as travel agents, this time technology will also affect high-paid occupations, such as executives and physicians. Machine learning and AI’s expertise will exceed humans’ and, as a result, more demanding tasks and decisions can be automated, making even high-skill professionals subject to the phenomenon, something not seen before. As profound as these changes may seem, they will not occur all at once, instead AI will slowly move into the workplace gradually replacing humans.
All in all, additionally to affecting companies financially, automation will deeply affect workers financially, because as labour becomes a less important factor in production, a majority of citizens may find the value of their labour insufficient to pay for a socially acceptable standard of living, which will require society to come up with solutions to prevent a part of the population from falling behind. AI and machine learning are successful as long as they create value for human lives. To safeguard human labour from becoming obsolete and inequality from increasing, it is vital that governments take an active role when it comes to defining policy. While it is important to stimulate investment in R&D, it is crucial to adopt a “humans first” position. Although it may be difficult to predict in which direction technological disruption may point towards, it will surely be impossible to go back from it, so legislation must be shaped in a way that advances in technology focused solely on corporate profit and disregard human capital are forfeit.
Sources: McKinsey, Statista, MarketLine, Forbes
Written by Diogo Alves, Lourenço Paramés and Tiago Rebelo
Scientific revision: Patrícia Cruz