If we want to profit from the increasing role that machines play in assisting human labor in the workplace, we will all need to adjust.
Automation and artificial intelligence (AI) are changing businesses; these developments will boost productivity and, in turn, support economic expansion. They will also assist in addressing “moonshot” societal challenges including health and climate change.
If we want to profit from the increasing role that machines play in assisting human labor in the workplace, we will all need to adjust.
Automation and artificial intelligence (AI) are changing businesses; these developments will boost productivity and, in turn, support economic expansion. They will also assist in addressing “moonshot” societal challenges including health and climate change.
These technological advancements will transform the workplace and the nature of work at the same time. Machines will take on more work from people and will be able to perform some things that humans are not capable of. Many occupations will change and prosper as a result, while others will disappear.
Although we hope that everyone will have enough work (barring any unanticipated events), society will have to adjust to significant labor shifts and disruptions. Employees will have to learn new skills and adjust to working with increasingly sophisticated machines. They may need to transition from declining careers to increasing and, on occasion, entirely new ones.
This executive briefing highlights some of the most important issues that organizations, individuals, and policymakers will need to face as it discusses the potential and the challenge of automation and artificial intelligence (AI) in the workplace. It makes use of the most current McKinsey Global Institute research.
Automation and AI are evolving more swiftly, which presents opportunities for businesses, the economy, and society.
While automation and artificial intelligence (AI) are not new, they are becoming more powerful due to recent technological developments. Our findings suggest that society needs these developments to support enterprises, foster economic expansion, and address some of the most pressing social issues. In conclusion:
Technology is developing quickly.
Beyond traditional industrial automation and sophisticated robots, new generations of more potent autonomous systems are appearing in scenarios ranging from autonomous cars on highways to automated checkouts at supermarkets. This achievement has been mostly attributed to advancements in many components and systems, such as software, sensors, and mechanics. AI has made especially notable progress in recent years as machine-learning algorithms have improved, made use of enormous increases in processing power, and leveraged the exponential growth of data available to train them. Incredible advancements are in the news these days, with many including superhuman abilities in natural language processing, computer vision, and difficult games like Go.
Before these technologies reach their full potential for the good of society and the global economy, there are still challenges to be solved.
AI and automation continue to face challenges. The need for massive volumes of training data and the challenge of “generalizing” algorithms across use cases are examples of technical limitations. Recent technologies are only now starting to address these challenges. Applying AI technology has its challenges as well. For example, it might be technically challenging to explain the results of machine learning algorithms; this is relevant for use cases that involve financial lending or legal applications. It is necessary to take into account issues such data privacy, illicit use, potential bias in the training data and algorithms, and security. Europe is leading the way with the new General Data Protection Regulation, which codifies stronger user rights over data collection and usage.
Businesses often face a new kind of challenge when implementing these technologies because of people, data availability, technology, and process readiness. Adoption is already unequal across nations and industries. The financial, automotive, and communications sectors are where AI is most commonly used. With between $15 and $23 billion in investments, the US led the world in AI investment in 2016. Asia came in second with between $8 and $12 billion, and Europe came in third with between $3 and $4 billion.
How AI and automation will alter the workplace
Even though automation and artificial intelligence (AI) benefit society and industry, we nevertheless need to be prepared for big changes in the workplace.
It is possible that about 50% of worker activities—not jobs—will be automated.
Our analysis of over 2000 job activities across over 800 jobs indicates that some work activity categories are simpler to automate than others. They comprise data collection and processing in addition to physical tasks carried out in highly organized and predictable environments. These makeup about half of all human activity, all industries together. Among the least vulnerable are stakeholder interactions, information sharing, and management of others.
Nearly every job will be impacted by automation, albeit only around 5% of them may be completely automated with the current set of technologies. We find that about 30% of tasks in 60% of all occupations have some potential for automation, and many more occupations have automatable portions of their constituent tasks. This means most workers—CEOs, mortgage brokers, and welders, among others—will operate along with rapidly developing machinery. These jobs will therefore most likely change in nature.
Employment gained: Moreover, over this time frame, jobs will be created.
Even when workers are displaced, additional jobs will be created as a result of the increased need for labor. We developed scenarios for labor demand until 2030 with the aid of several demand-inducing factors, including rising wages, rising healthcare costs, and continued or increased investment in energy, infrastructure, and technology research and deployment. By 2030, the number of jobs lost was expected to be more than balanced by the addition of 555 million to 890 million jobs, or between 21 and 33 percent of the global workforce. Some of the largest benefits would accrue to the economy of developing countries like India, where the working-age population is already growing rapidly.
The economy will keep growing and creating jobs as a result of increased productivity growth and company dynamism. If the past is any indication, a great number of other, as yet unimagined professions will emerge and might account for as much as 10% of all jobs created by 2030. Moreover, technology has historically created net employment. For example, the introduction of the personal computer in the 1970s and 1980s resulted in the employment of millions of people in the semiconductor industry as well as information analysts, customer service representatives, and developers of various software and apps.
Jobs changed: More jobs will be altered than created as machines replace human labor in the workplace.
As machines replace some human labor, partial automation will become more common. AI algorithms, which, for example, can read diagnostic scans with a high degree of accuracy, will help doctors diagnose patient problems and determine the best course of therapy. In other industries, jobs involving repetitive tasks can shift to controlling and maintaining automated systems. Once responsible for lifting and piling merchandise, Amazon employees are now robot operators, monitoring the robotic arms and resolving issues such as a disruption in the flow of goods.
Significant changes in the workforce and challenges
We estimate that by 2030, there will be sufficient labor to ensure full employment based on most of our scenarios; nevertheless, the changes resulting from the widespread use of automation and artificial intelligence will be significant. The mix of employment will change along with the requirements for education and ability. Work will need to be rebuilt to ensure that humans and machines collaborate as successfully as possible.
Workers will need a variety of skills to prosper in the workplace of the future.
The shift in workforce skills that has been taking place over the last 15 years will be accelerated by automation. Programming is one of the highly technical talents that will see rapid demand. Higher level social, emotional, and cognitive skills including creativity, critical analysis, and complex information processing will also be in greater demand. Basic digital abilities are in greater demand than ever, and this trend will only get stronger. Although the need for manual and physical skills will decline in many nations, they will still make up the majority of worker skills in 2030 (Exhibit 3). This will make the already urgent problems with workforce skills and the need for new credentialing systems worse. Even while some creative solutions are starting to emerge, larger solutions will need to be found.
Many workers will most likely need to change employment.
Our research indicates that the middle scenario projects that 3% to 14% of the global workforce will have to change occupational categories by 2030. Certain firms and industries will experience some of these changes, but many of them will affect whole industries or perhaps entire nations. Employment requiring physical work in highly organized settings, such as data processing and collection, will decline. There will be an increase in the professions of managers, who carry out jobs that are hard to automate, and plumbers, who operate in physically unpredictable environments. Shortly, there will be a greater need for workers in several professions, including teaching, nursing assistant, computing, and other fields.
Workflows will change when more people use the machines next to them in the workplace.
As software and artificial gadgets are more thoroughly incorporated into the workplace and allow for the collaboration of humans and robots, workflows and workspaces will continue to evolve. Cashiers can become checkout assistance helpers who can aid with questions or system difficulties, for example, if self-checkout equipment is installed in establishments. More system-level solutions will necessitate a complete rethinking of the environment and process. If certain portions of a warehouse are designed primarily to house robots and other areas to facilitate safe human-machine interaction, the layout of the space may change significantly.
Automation is expected to put pressure on average wages in industrialized economies.
The shift in the occupational mix will most likely result in wage pressure. The majority of middle-class jobs that are currently available in developed countries are highly automatable fields like accountancy or manufacturing, which are expected to become extinct. While high-paying jobs will be in great demand, especially for highly talented individuals in tech, medical, or other industries, many of the new jobs that are expected to be created, such as those for teaching and nursing aides, often have lower pay scales. Automation carries the risk of intensifying social and political unrest and exacerbating the wage gap, income inequality, and stagnant income growth that have characterized developed nations over the past ten years.
There are already problems with the workforce despite these upcoming challenges.
Most countries now find it difficult to equip and train their workforces in a way that meets the needs of modern enterprises. The OECD has seen a decline in worker education and training spending over the last 20 years. The amount spent on worker transition and relocation assistance as a percentage of GDP has also been declining. One thing we’ve learned in the last ten years is that, even though globalization may have benefitted consumers and economic growth, workers’ pay and job displacement were not adequately considered. Most assessments, including ours, suggest that over the next few decades, the severity of these issues is likely to worsen. Pay has previously been demonstrated to be impacted by significant changes in the workforce. For instance, during the 19th-century Industrial Revolution, wages in the United Kingdom stagnated despite increased productivity for almost 50 years. This phenomenon is referred to as “Engels’ Pause” (PDF-690KB), named for the German philosopher who first observed it.
Machines will take on more work from people and will be able to perform some things that humans are not capable of. Many occupations will change and prosper as a result, while others will disappear.
Although we hope that everyone will have enough work (barring any unanticipated events), society will have to adjust to significant labor shifts and disruptions. Employees will have to learn new skills and adjust to working with increasingly sophisticated machines. They may need to transition from declining careers to increasing and, on occasion, entirely new ones.
This executive briefing highlights some of the most important issues that organizations, individuals, and policymakers will need to face as it discusses the potential and the challenge of automation and artificial intelligence (AI) in the workplace. It makes use of the most current McKinsey Global Institute research.
Automation and AI are evolving more swiftly, which presents opportunities for businesses, the economy, and society.
While automation and artificial intelligence (AI) are not new, they are becoming more powerful due to recent technological developments. Our findings suggest that society needs these developments to support enterprises, foster economic expansion, and address some of the most pressing social issues. In conclusion:
Technology Is Developing Quickly
Beyond traditional industrial automation and sophisticated robots, new generations of more potent autonomous systems are appearing in scenarios ranging from autonomous cars on highways to automated checkouts at supermarkets. This achievement has been mostly attributed to advancements in many components and systems, such as software, sensors, and mechanics. AI has made especially notable progress in recent years as machine-learning algorithms have improved, made use of enormous increases in processing power, and leveraged the exponential growth of data available to train them. Incredible advancements are in the news these days, with many including superhuman abilities in natural language processing, computer vision, and difficult games like Go.
Before these technologies reach their full potential for the good of society and the global economy, there are still challenges to be solved.
AI and automation continue to face challenges. The need for massive volumes of training data and the challenge of “generalizing” algorithms across use cases are examples of technical limitations. Recent technologies are only now starting to address these challenges. Applying AI technology has its challenges as well. For example, it might be technically challenging to explain the results of machine learning algorithms; this is relevant for use cases that involve financial lending or legal applications. It is necessary to take into account issues such data privacy, illicit use, potential bias in the training data and algorithms, and security. Europe is leading the way with the new General Data Protection Regulation, which codifies stronger user rights over data collection and usage.
Businesses often face a new kind of challenge when implementing these technologies because of people, data availability, technology, and process readiness. Adoption is already unequal across nations and industries. The financial, automotive, and communications sectors are where AI is most commonly used. With between $15 and $23 billion in investments, the US led the world in AI investment in 2016. Asia came in second with between $8 and $12 billion, and Europe came in third with between $3 and $4 billion.
How AI And Automation Will Alter The Workplace
Even though automation and artificial intelligence (AI) benefit society and industry, we nevertheless need to be prepared for big changes in the workplace.
It is possible that about 50% of worker activities—not jobs—will be automated.
Our analysis of over 2000 job activities across over 800 jobs indicates that some work activity categories are simpler to automate than others. They comprise data collection and processing in addition to physical tasks carried out in highly organized and predictable environments. These make up about half of all human activity, all industries together. Among the least vulnerable are stakeholder interactions, information sharing, and management of others.
Nearly every job will be impacted by automation, albeit only around 5% of them may be completely automated with the current set of technologies. We find that about 30% of tasks in 60% of all occupations have some potential for automation, and many more occupations have automatable portions of their constituent tasks. This means most workers—CEOs, mortgage brokers, and welders, among others—will operate along with rapidly developing machinery. These jobs will therefore most likely change in nature.
Employment gained: Moreover, over this time frame, jobs will be created.
Even when workers are displaced, additional jobs will be created as a result of the increased need for labor. We developed scenarios for labor demand until 2030 with the aid of several demand-inducing factors, including rising wages, rising healthcare costs, and continued or increased investment in energy, infrastructure, and technology research and deployment. By 2030, the number of jobs lost was expected to be more than balanced by the addition of 555 million to 890 million jobs, or between 21 and 33 percent of the global workforce. Some of the largest benefits would accrue to the economy of developing countries like India, where the working-age population is already growing rapidly.
The economy will keep growing and creating jobs as a result of increased productivity growth and company dynamism. If the past is any indication, a great number of other, as yet unimagined professions will emerge and might account for as much as 10% of all jobs created by 2030. Moreover, technology has historically created net employment. For example, the introduction of the personal computer in the 1970s and 1980s resulted in the employment of millions of people in the semiconductor industry as well as information analysts, customer service representatives, and developers of various software and apps.
Jobs changed: More jobs will be altered than created as machines replace human labor in the workplace.
As machines replace some human labor, partial automation will become more common. AI algorithms, which, for example, can read diagnostic scans with a high degree of accuracy, will help doctors diagnose patient problems and determine the best course of therapy. In other industries, jobs involving repetitive tasks can shift to controlling and maintaining automated systems. Once responsible for lifting and piling merchandise, Amazon employees are now robot operators, monitoring the robotic arms and resolving issues such as a disruption in the flow of goods.
Significant Changes In The Workforce And Challenges
We estimate that by 2030, there will be sufficient labor to ensure full employment based on most of our scenarios; nevertheless, the changes resulting from the widespread use of automation and artificial intelligence will be significant. The mix of employment will change along with the requirements for education and ability. Work will need to be rebuilt to ensure that humans and machines collaborate as successfully as possible.
Workers will need a variety of skills to prosper in the workplace of the future.
The shift in workforce skills that has been taking place over the last 15 years will be accelerated by automation. Programming is one of the highly technical talents that will see rapid demand. Higher level social, emotional, and cognitive skills including creativity, critical analysis, and complex information processing will also be in greater demand. Basic digital abilities are in greater demand than ever, and this trend will only get stronger. Although the need for manual and physical skills will decline in many nations, they will still make up the majority of worker skills in 2030 (Exhibit 3). This will make the already urgent problems with workforce skills and the need for new credentialing systems worse. Even while some creative solutions are starting to emerge, larger solutions will need to be found.
Many workers will most likely need to change employment.
Our research indicates that the middle scenario projects that 3% to 14% of the global workforce will have to change occupational categories by 2030. Certain firms and industries will experience some of these changes, but many of them will affect whole industries or perhaps entire nations. Employment requiring physical work in highly organized settings, such as data processing and collection, will decline. There will be increase in the professions of managers, who carry out jobs that are hard to automate, and plumbers, who operate in physically unpredictable environments. In the near future, there will be a greater need for workers in a number of professions, including teaching, nursing assistant, computing, and other fields.
Workflows will change when more people use the machines next to them in the workplace.
As software and artificial gadgets are more thoroughly incorporated into the workplace and allow for the collaboration of humans and robots, workflows and workspaces will continue to evolve. Cashiers can become checkout assistance helpers who can aid with questions or system difficulties, for example, if self-checkout equipment is installed in establishments. More system-level solutions will necessitate a complete rethinking of the environment and process. If certain portions of a warehouse are designed primarily to house robots and other areas to facilitate safe human-machine interaction, the layout of the space may change significantly.
Automation is expected to put pressure on average wages in industrialized economies.
The shift in the occupational mix will most likely result in wage pressure. The majority of middle-class jobs that are currently available in developed countries are highly automatable fields like accountancy or manufacturing, which are expected to become extinct. While high-paying jobs will be in great demand, especially for highly talented individuals in the tech, medical, or other industries, many of the new jobs that are expected to be created, such as those for teaching and nursing aides, often have lower pay scales. Automation carries the risk of intensifying social and political unrest and exacerbating the wage gap, income inequality, and stagnant income growth that have characterized developed nations over the past ten years.
There are already problems with the workforce in spite of these upcoming challenges.
Most countries now find it difficult to equip and train their workforces in a way that meets the needs of modern enterprises. The OECD has seen a decline in worker education and training spending over the last 20 years. The amount spent on worker transition and relocation assistance as a percentage of GDP has also been declining. One thing we’ve learned in the last ten years is that, even though globalization may have benefitted consumers and economic growth, workers’ pay and job displacement were not adequately considered. Most assessments, including ours, suggest that over the next few decades, the severity of these issues is likely to worsen. Pay has previously been demonstrated to be impacted by significant changes in the workforce. For instance, during the 19th-century Industrial Revolution, wages in the United Kingdom stagnated despite increased productivity for almost 50 years. This phenomena is referred to as “Engels’ Pause” (PDF-690KB), named for the German philosopher who first observed it.