Artificial intelligence
AI Automation And The Future Of Work:
Ten Things To Solve For:
As machines increasingly complement human labor in workplace. We will all need to adjust to reap the benefits.
Transformation
Automation and artificial intelligence are transforming businesses and it will contribute to economic growth via
contributions to productivity. They will also help address “moonshot” societal challenges in areas from health to climate change.
Change in nature of work
These technologies will also transform the nature of work and workplace itself. Machines will be able to carry out more of the tasks done by humans. Complement the work that humans do. It even perform some tasks that go beyond what humans can do. As a result of it some occupations will decline, others will grow, and many more may change.
Adaptation of Skills
While we believe there will be enough work to go around (barring extreme scenarios), society will need to grapple with significant workforce transitions and dislocation. Workers will need to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace. They may have to move from declining occupations to growing. In some cases new occupations.
Issues Of Automation
This executive briefing, which draws on the latest research examines both the promises and the challenges of automation and AI in the workplace and outlines some of the critical issues that policy makers, companies and the individuals will need to solve for.
Opportunity
Accelerating progress in AI and automation is creating opportunities for businesses, the economy, and society
How AI and automation will affect work
Ten things to solve for
Accelerating progress in AI and automation is creating opportunities for businesses, the economy, and society
Automation and AI are not new but recent technological progress is pushing the frontier of what machines can do. Our research suggests that society needs these improvements to provide value for businesses, contribute to economic growth, and make once unimaginable progress on some of our most difficult societal challenges.
Rapid technological progress
Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to the automated check outs in grocery stores. Much of this progress has been driven by the improvements in systems and components including mechanics, sensors and softwares.
AI has made especially large strides in recent years as machine learning algorithms have become more sophisticated and it made use of huge increases in computing power and of the exponential growth in data available to train them. Spectacular breakthroughs are making headlines involving beyond human capabilities in computer vision, natural language processing, and complex games such as Go.
Potential to transform businesses and contribute to economic growth
These technologies are already generating value in various products and services and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent
transactions, and more. The latest generation of AI advances, including techniques that address taxonomy, estimation, and clustering problems, promises significantly more value still. An analysis we conducted of several hundred AI use cases found that the most advanced deep learning techniques deploying artificial neural networks could accounts for as much as $3.5 trillion to $5.8 trillion in annual value, or 40 percent of the value created by all analytics techniques (Exhibit 1).
Increasing Global Prosperity
Deployment of AI and automation technologies can do a lot to lift the global economy and to increase global prosperity. At a time, when aging and falling birth rates are acting as a drag on growth. Labor productivity growth, a key driver of economic growth has slowed in many economies, dropping to an average of 0.5 percent in 2010–2014 from 2.4 percent a decade earlier in the United States and major European economies.
In the aftermath of the 2008 financial crisis after a previous productivity boom had waned. AI and automation have the potential to reverse that decline: productivity growth could potentially reach 2 percent annually over the next decade, with 60 percent of this increase from digital opportunities.
Potential to help tackle several societal moonshot challenges
AI is also being used in areas ranging from material science to the medical research and the climate science. Application of technologies in these and other disciplines could help tackle societal moonshot challenges.
For example, researchers have developed an algorithm that could reduce diagnostic times for intracranial hemorrhaging by up to 96% . Researchers are using machine learning to more accurately weight the climate models used by the Intergovernmental Panel on Climate Change.
Challenges
Challenges remains before these technologies can live up to their potential for the good of the economy and society everywhere
AI and automation still face challenges. The limitations are partly technical such as the need for massive training data and difficulties “generalizing” algorithms across use cases. Recent innovations are just starting to address these issues. Other challenges are in use of AI techniques.
For example explaining decisions made by machine learning algorithms is technically challenging which particularly matters to use cases involving financial lending or legal applications. Potential bias in training data and algorithms as well as data privacy, malicious use and security are all issues that must be addressed. Europe is leading with the new General Data Protection Regulation that codifies more rights for users over data collection and usage.
A different sort of challenge concerns the ability of organizations to adopt these technologies where people, data availability, technology and process readiness often make it difficult. Adoption is already uneven across sectors and countries. The finance, automotive, and telecommunications sectors lead AI adoption. Among countries, US investment in AI ranked first at $15 billion to $23 billion in 2016 followed by Asia’s investments of $8 billion to $12 billion with Europe lagging behind at $3 billion to $4 billion.
How AI and automation will affect work
AI and automation is beneficial for business and society. We will have to prepare for major disruptions to work.
About half of the activities (not jobs) carried out by workers could be automated
Our analysis of more than 2000 work activities across more than 800 occupations shows that certain categories of activities are more easily automatable than others. They include physical activities in highly predictable and structured environments as well as data collection and data processing. These account for roughly half of the activities that people do across all sectors. The least susceptible categories include managing others and providing expertise and interfacing with stakeholders.
Effects on Occupation
Nearly all occupations will be affected by automation, but only about 5 percent of occupations could be fully automated by currently demonstrated technologies. Many more occupations have portions of their constituent activities that are automatable: we find that about 30 percent of the activities in 60 percent of all occupations could be automated. This means that most workers from welders to mortgage brokers to CEOswill work alongside rapidly evolving machines. The nature of these occupations will likely change as a result.
Jobs lost:
Some occupations will see significant declines by 2030
Automation will displace some workers. We have found that around 15 percent of the global workforce, or about 400 million workers, could be displaced by automation in the period 2016–2030. It may reflects our midpoint scenario in projecting the pace and scope of adoption.
Under the fastest scenario we have modeled that figure rises to 30 percent or 800 million workers. In our slowest adoption scenario only about 10 million people would be displaced, close to zero percent of the global workforce
Factors That Impacts The Scope Of Automation
The wide range underscores the multiple factors that will impact the pace and scope of AI and automation adoption. Technical feasibility of automation is only the first influencing factor. Other factors include the cost of deployment; labor-market dynamics, including labor-supply quantity, quality, and the associated wages the benefits beyond labor substitution that contribute to business cases for adoption and finally social norms and acceptance.
Adoption will continue to vary significantly across countries and sectors because of differences in the above factors, especially labor-market dynamics in advanced economies with relatively high wage levels, such as France, Japan, and the United States, automation could displace 20 to 25 percent of the workforce by 2030, in a midpoint adoption scenario more than double the rate in India.
Jobs gained:
In the same period, jobs will also be created
Even as workers are displaced still there will be growth in demand for work and consequently jobs. We developed scenarios for labor demand to 2030 from several catalysts of demand for work including rising incomes, increased spending on healthcare and continuing investment in infrastructure, energy, technology development and deployment
These scenarios showed a range of additional labor demand of between 21 percent to 33 percent of the global workforce (555 million and 890 million jobs) to 2030, more than offsetting the numbers of jobs lost. Some of the largest gains will be in emerging economies such as India, where the working-age population is already growing rapidly.
AI will change the scenario of job
Additional economic growth, including from business dynamism and rising productivity growth, will also continue to create jobs. Many other new occupations that we cannot currently imagine will also emerge and may account for as much as 10 percent of jobs created by 2030, if history is a guide. Moreover, technology itself has historically been a net job creator
. For example, the introduction of the personal computer in the 1970s and 1980s Created millions of jobs not just for semiconductor makers but also for software and app developers of all types, customer-service representatives, and information analysts.
Jobs changed:
More jobs than those lost or gained will be changed as machines complement human labor in the workplace
Partial automation will become more prevalent as machines complement human labor. For example, AI algorithms that can read diagnostic scans with a high degree of accuracy will help doctors diagnose patient cases and identify suitable treatment.
In other fields, jobs with repetitive tasks could shift toward a model of managing and troubleshooting automated systems. At retailer Amazon, employees who previously lifted and stacked objects are becoming robot operators, monitoring the automated arms and resolving issues such as an interruption in the flow of objects.
Key workforce transitions and challenges
While we expect there will be enough work to ensure full employment in 2030 based on most of our scenarios.The transitions that will accompany automation and AI adoption will be significant. The mix of occupations will change as will be the skill and educational requirements. Work will need to be redesigned to ensure that humans work alongside machines most effectively.
Skilled workers:
Workers will need different skills to thrive in the workplace of the future
Automation will accelerate the shift in required workforce skills we have seen over the last 15 years. Demand for advanced technological skills such as programming will grow rapidly. Social, emotional and higher cognitive skills such as creativity, critical thinking and complex information processing, will also see growing demand. Basic digital skills demand has been increasing and that trend will continue and accelerate. Demand for physical and manual skills will decline but will remain the single largest category of workforce skills in 2030 in many countries . This will put additional pressure on the already existing workforce skills challenge as well as the need for new credentialing systems. While some innovative solutions are emerging, solutions that can match the scale of the challenge will be needed.
Many workers will likely need to change occupations
Our research suggests that in a midpoint scenario around 3 percent of the global workforce will need to change occupational categories by 2030 though scenarios range from about 0 to 14 percent. Some of these shifts will happen within companies and sectors but many will occur across sectors and even geographies. Occupations made up of physical activities in highly structured environments or in data processing or collection will see declines. Growing occupations will include those with difficult to automate activities such as managers and those in unpredictable physical environments such as plumbers. Other occupations that will see increasing demand for work include teachers, nursing aides, and tech and other professionals.
Workplaces and workflows will change as more people work alongside machines
As intelligent machines and software are integrated more deeply into the workplace, workflows and workspaces will continue to evolve to let humans and machines to work together. As self checkout , machines are introduced in stores. for example, cashiers can become checkout assistance helpers, who can help answer questions or troubleshoot the machines. More system level solutions will prompt rethinking of the entire workflow and workspace. Warehouse design may change significantly as some portions are designed to accommodate primarily robots and others to facilitate safe human-machine interaction.
Skill shifts:
Automation and the future of the workforce
Automation will likely put pressure on average wages in advanced economies
The occupational mix shifts will likely put pressure on wages. Many of the current middle wage jobs in advanced economies are dominated by highly automatable activities such as in manufacturing or in accounting which are likely to decline. High-wage jobs will grow significantly especially for high-skill medical and tech or other professionals but a large portion of jobs expected to be created including teachers and nursing aides typically have lower wage structures. The risk is that automation could exacerbate wage polarization, income inequality and the lack of income advancement that has characterized the past decade across advanced economies, stoking social and political tensions.
In the face of these looming challenges workforce challenges already exist
Most countries already face the challenge of adequately educating and training their workforces to meet the current requirements of employers. Across the OECD spending on worker education and training has been declining over the last two decades. Spending on worker transition and dislocation assistance has also continued to shrink as a percentage of GDP. One lesson of the past decade is that while globalization may have benefited economic growth and people as consumers the wage and dislocation effects on workers were not adequately addressed. Most analyses, including our own suggests that the scale of these issues is likely to grow in the coming decades. We have also seen in the past that large scale workforce transitions can have a lasting effect on wages during the 19th century Industrial Revolution, wages in the United Kingdom remained stagnant for about half a century despite rising productivity ,a phenomenon known as “Engels’ Pause,” (PDF–690KB) after the German philosopher who identified it.
Ten things to solve for
In the search for appropriate measures and policies to address these challenges, we should not seek to roll back or slow diffusion of the technologies. Companies and governments should harness automation and AI to benefit from the enhanced performance and productivity contributions as well as the societal benefits. These technologies will create the economic surpluses that will help societies manage workforce transitions. Rather, the focus should be on ways to ensure that the workforce transitions are as smooth as possible. This is likely to require actionable and scalable solutions in several key areas:
Required actions
Ensuring robust economic and productivity growth. Strong growth is not the magic answer for all the challenges posed by the automation but it is a prerequisite for job growth and increasing prosperity.
Productivity growth is a key contributor to economic growth. Therefore, unlocking investment and demand, as well as embracing automation for its productivity contributions and it is critical.
Fostering business dynamism.
Accelerating the rate of new business formation and the growth and competitiveness of businesses, large and small, will require simpler and evolved regulations, tax and other incentives.
Evolving education systems
learning for a changed workplace. Policy makers working with education providers (traditional and nontraditional) and employers themselves could do more to improve basic STEM skills through the school sy
stems and improved on-the-job training. A new emphasis is needed on creativity, critical and systems thinking, and adaptive and life-long learning. There will need to be solutions at scale.
Investment
Investing in human capital. Reversing the trend of low, and in some countries, declining public investment in worker training is critical.
Through tax benefits and other incentives, policy makers can encourage companies to invest in human capital, including job creation, learning and capability building, and wage growth, similar to incentives for private sector to invest in other types of capital including R&D.
Improving labor-market dynamism
. Information signals that enable matching of workers to work, credentialing, could all work bett
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