We will all need to adapt if we are to benefit from the growing role that machines play in complementing human labor in the workplace.
Businesses are being transformed by automation and artificial intelligence (AI), which will also increase productivity and hence contribute to economic growth. Additionally, they will support efforts to address “moonshot” societal issues ranging from climate change to health.
These technologies will simultaneously change the nature of employment and the workplace. More human duties will be completed by machines, who will also be able to undertake some tasks that are beyond the capabilities of humans. As a result, many professions will alter and thrive, while others will perish.
While we anticipate that there will be sufficient work for everyone (without unforeseen circumstances), society will have to deal with considerable labor shifts and dislocation. Workers will need to pick up new skills and get used to working alongside machines that are more and more adept. They could have to switch from falling occupations to rising and, occasionally, brand-new ones.
This executive briefing addresses both the promise and the problem of automation and AI in the workplace and identifies some of the crucial concerns that policy makers, businesses, and individuals will need to address. It draws on the most recent research from the McKinsey Global Institute.
AI and automation are developing more quickly, opening up potential for enterprises, the economy, and society
Although automation and AI are not new, current technology advancements are expanding the capabilities of machines. According to our research, society needs these advancements in order to benefit businesses, promote economic growth, and solve some of the most severe societal problems. To sum up:
Rapid advancement of technology
New generations of more powerful autonomous systems are emerging in situations ranging from autonomous automobiles on roadways to automated check-outs in grocery shops, going beyond conventional industrial automation and advanced robots. Improvements in systems and elements, including mechanics, sensors, and software, have been largely responsible for this success. As machine-learning algorithms have advanced, utilized vast gains in computer power, and utilized the exponential rise of data available to train them, AI has made particularly significant strides in recent years. Amazing developments are making news, many of which involve superhuman powers in computer vision, natural language processing, and challenging games like Go.
There are still obstacles to overcome before these technologies can fully realize their potential for the benefit of the global economy and society.
Automation and AI still have problems. Technical restrictions include the necessity for enormous amounts of training data and the difficulty in “generalizing” algorithms across use cases. These concerns are only now beginning to be addressed by recent technologies. The application of AI technology also presents difficulties. It can be technically difficult to explain conclusions produced by machine learning algorithms, for instance, which matters for use cases involving financial lending or legal applications. Issues including potential bias in the training data and algorithms, data privacy, criminal use, and security must all be taken into consideration. With the new General Data Protection Regulation, which codifies stronger user rights over data collection and use, Europe is taking the lead.
The ability of enterprises to implement these technologies presents a new type of issue, as it is frequently tough due to people, data availability, technology, and process preparedness. Across industries and nations, adoption is already uneven. AI use is most prevalent in the financial, automotive, and communications industries. In terms of individual nations, the US placed first in AI investment in 2016 with investments between $15 and $23 billion, followed by Asia with investments between $8 and $12 billion, and Europe with investments between $3 and $4 billion.
How automation and AI will change the workplace
Even while AI and automation have positive effects on business and society, we still need to be ready for significant changes in the workplace.
Approximately 50% of worker activities (not jobs) might be automated.
Some categories of work activities are easier to automate than others, according to our examination of more than 2000 job activities spanning more than 800 occupations. They consist of physical activities performed in highly structured and predictable surroundings, as well as data gathering and processing. About half of all human activity, across all sectors, are comprised of these. Managing others, giving knowledge, and interacting with stakeholders are among the least vulnerable categories.
Automation will have an impact on almost all jobs, although only approximately 5% of them may be totally mechanized using the technologies that are currently available. We discover that around 30% of the tasks in 60% of all occupations could be automated, with many more occupations having sections of their constituent activities that are automatable. This implies that the majority of workers, including CEOs, mortgage brokers, and welders, will collaborate with quickly advancing machinery. As a result, these jobs will probably change in nature.
Employment gained: Additionally, employment will be produced throughout this time period.
The need for labor will increase, which will lead to more employment being created even as employees are displaced. With the help of a number of demand-inducing factors, such as rising wages, rising healthcare costs, and continued or stepped-up investment in infrastructure, energy, and technology research and deployment, we created scenarios for labor demand until 2030. Between 555 million and 890 million jobs, or between 21 percent and 33 percent of the world’s workforce, were projected to be added to the labor market by 2030, more than balancing the number of jobs lost. The economies of emerging nations like India, where the working-age population is already expanding quickly, will see some of the biggest gains.
Jobs will continue to be created as a result of continued economic expansion, including corporate dynamism and increased productivity growth. If history is a guide, many additional new professions that we cannot currently envisage will also develop and may represent as much as 10% of the jobs produced by 2030. Furthermore, traditionally, technology has been a net employment producer. As an illustration, the introduction of the personal computer in the 1970s and 1980s led to the creation of millions of employment for information analysts, customer service agents, and a variety of software and app developers, in addition to semiconductor manufacturers.
Jobs transformed: As machines supplement human labor in the workplace, more jobs will be changed than those lost or gained.
The prevalence of partial automation will increase as machines replace some human labor. Doctors will be assisted in diagnosing patient problems and choosing the best course of therapy by AI algorithms, which, for instance, can read diagnostic scans with a high degree of accuracy. Jobs with repetitive duties in other industries might transition to a model of managing and fixing automated systems. Employees at online retailer Amazon who once lifted and piled items are now robot operators, keeping an eye on the robotic arms and fixing problems like a pause in the flow of goods.
Important workforce changes and difficulties
Based on the majority of our scenarios, we predict that there will be enough labor to guarantee full employment by 2030, but the changes brought on by the deployment of automation and AI will be profound. Along with changing skill and educational requirements, the mix of occupations will also shift. To make sure that humans and machines work together as effectively as possible, work will need to be rebuilt.
For workers to succeed in the workplace of the future, they will require various talents.
Automation will hasten the change in workforce skills that has been occurring over the past 15 years. The demand for highly technical skills like programming will rise quickly. Demand will also increase for higher order social, emotional, and cognitive abilities like creativity, critical analysis, and sophisticated information processing. The demand for fundamental digital skills has been rising, and this tendency will only intensify. In many countries, the demand for physical and manual skills will diminish but will still account for the largest category of worker skills in 2030 (Exhibit 3). The already pressing issue of workforce skills and the requirement for new credentialing systems will be exacerbated by this. While some novel solutions are emerging, it will be necessary to find ones that can handle the size of the problem.
There will probably be a need for many workers to switch jobs.
According to our analysis, the midpoint scenario predicts that by 2030, between 3% and 14% of the world’s workforce will need to switch occupational categories. While many of these changes will occur across industries and even countries, some will take place within specific businesses and industries. There will be a drop in jobs involving physical labor in highly structured environments or in data processing or collecting. Managers, who perform tasks that are difficult to automate, and plumbers, who work in physically uncertain surroundings, will both experience growth in their respective fields. Teachers, nursing assistants, computer professionals, and other professions will all experience an increase in demand for labor in the near future.
As more individuals use machines beside them in the workplace, workflows will shift.
Workflows and workspaces will continue to change as intelligent devices and software become more fully integrated into the workplace, making it possible for humans and machines to collaborate. For instance, if self-checkout equipment are implemented in stores, cashiers can transition into checkout assistance helpers who can assist with inquiries or system troubleshooting. The entire workflow and environment will need to be rethought as a result of more system-level solutions. The layout of a warehouse may alter dramatically if some areas are planned primarily to house robots and other areas to enable secure human-machine interaction.
In industrialized economies, automation is projected to exert pressure on average wages.
The changes in the occupational mix will probably put pressure on wages. Many of the middle-wage occupations that are currently available in industrialized nations are dominated by highly automatable professions like manufacturing or accounting, which are predicted to disappear. Although there will be a major increase in high-paying positions, particularly for highly skilled professionals in the medical, tech, or other fields, many of the new occupations that are anticipated to be created, like those for teachers and nursing assistants, often have lower pay scales. Automation has the potential of escalating the wage disparity, income inequality, and lack of income growth that have defined industrialized countries over the previous ten years and igniting social and political conflicts.
Despite these impending difficulties, workforce issues already exist.
The majority of nations currently struggle to appropriately educate and equip their labor forces to satisfy the demands of businesses today. Over the past 20 years, spending on worker education and training has decreased across the OECD. As a proportion of GDP, spending on worker transition and relocation aid has likewise continued to decline. One lesson from the past ten years is that, despite the fact that consumers and economic growth may have benefited from globalization, the consequences of wages and job displacement on employees were not fully taken into account. The majority of evaluations, including our own, indicate that the severity of these problems is expected to worsen over the next few decades. Large-scale workforce changes have also been shown to have an impact on wages in the past. For example, during the Industrial Revolution of the 19th century, wages in the United Kingdom stagnated for about 50 years despite rising productivity, a phenomenon known as “Engels’ Pause” (PDF-690KB) after the German philosopher who first noticed it.