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Ai, Automation, and the Future of Work: Ten Things to Solve For

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Ai, Automation, and the Future of Work: Ten Things to Solve For AI, AUTOMATION, AND THE FUTURE OF WORK: TEN THINGS TO SOLVE FOR BRIEFING NOTE PREPARED FOR THE TECH4GOOD SUMMIT, ORGANIZED BY THE FRENCH PRESIDENCY JUNE 2018 Automation and artificial intelligence (AI) are 1. Accelerating progress in AI and transforming businesses and will contribute to automation is creating opportunities for economic growth via contributions to productivity. businesses, the economy, and society They will also help address “moonshot” societal Automation and AI are not new, but recent challenges in areas from health to climate change. technological progress is pushing the frontier of At the same time, these technologies will transform what machines can do. Our research suggests the nature of work and the workplace itself—which that society needs these improvements to provide is the focus of this briefing note. Machines will value for businesses, contribute to economic be able to carry out more of the tasks done by growth, and make once unimaginable progress humans, complement the work that humans do, on some of our most difficult societal challenges.1 and even perform some tasks that go beyond what In summary: humans can do. As a result, some occupations Rapid technological progress will decline, others will grow, and many more will Beyond traditional industrial automation and change. While we believe there will be enough advanced robots, new generations of more work to go around (barring extreme scenarios), capable autonomous systems are appearing in society will need to grapple with significant environments ranging from autonomous vehicles workforce transitions and dislocation. Workers on roads to automated check-outs in grocery will need to acquire new skills and adapt to the stores.2 Much of this progress has been driven increasingly capable machines alongside them by improvements in systems and components, in the workplace. They may have to move from including mechanics, sensors, and software. declining occupations to growing and, in some AI has made especially large strides in recent cases, new occupations. This briefing note, which years, as machine-learning algorithms have draws on the latest research from the McKinsey become more sophisticated and made use of Global Institute, examines both the promise huge increases in computing power and of the and the challenge of automation and AI in the exponential growth in data available to train workplace and outlines some of the critical issues algorithms. Spectacular breakthroughs are that policy makers, companies, and individuals will making headlines, many involving beyond-human need to solve for. capabilities in computer vision, natural language Challenges remain before these technologies processing, and complex games such as Go. can live up to their potential for the good of the economy and society Potential to transform businesses and AI and automation still face challenges. The contribute to economic growth limitations are partly technical, such as the need for These technologies are already generating value massive training data and difficulties “generalizing” in various products and services, and companies algorithms across use cases. Recent innovations across sectors use them in an array of processes are just starting to address these issues. Other to personalize product recommendations, find challenges are in the use of AI techniques. For anomalies in production, identify fraudulent example, explaining decisions made by machine transactions, and more. The latest generation of learning algorithms is technically challenging, AI advances, including techniques that address which particularly matters for use cases involving classification, estimation, and clustering problems, financial lending or legal applications. Potential promises significantly more value still. An analysis bias in the training data and algorithms, as well as we conducted of several hundred AI use cases data privacy, malicious use, and security are all found that the most advanced deep learning issues that must addressed.8 Europe is leading techniques deploying artificial neural networks with the new General Data Protection Regulation, could account for as much as $3.5 trillion to which codifies more rights for users over data $5.8 trillion in annual value, or 40 percent of the collection and usage. A different sort of challenge value created by all analytics techniques.3 concerns the ability of organizations to adopt Deployment of AI and automation technologies these technologies, where people, data availability, can do much to lift the global economy and technology, and process readiness often make increase global prosperity, at a time when aging it difficult. Adoption is already uneven across and falling birth rates are acting as a drag on sectors and countries. The finance, automotive, growth. Labor productivity growth, a key driver of and telecommunications sectors lead AI adoption. economic growth, has slowed in many economies, Among countries, US investment in AI ranked first dropping to an average of 0.5 percent in 2010–14 at $15 billion to $23 billion in 2016, followed by from 2.4 percent a decade earlier in the United Asia’s investments of $8 billion to $12 billion, with States and major European economies, in the Europe lagging at $3 billion to $4 billion.9 aftermath of the 2008 financial crisis after a 2. How AI and automation will affect work previous productivity boom had waned. AI and Even as AI and automation bring benefits to automation have the potential to reverse that business and society, we will need to prepare for decline: productivity growth could potentially major disruptions to work. reach 2 percent annually over the next decade, with 60 percent of this increase from digital About half of the activities (not jobs) carried out opportunities.4 by workers could be automated Our analysis of more than 2000 work activities Potential to help tackle several societal across more than 800 occupations shows that “moonshot” challenges certain categories of activities are more easily AI is also being used in areas ranging from material automatable than others. They include physical science to medical research and climate science. activities in highly predictable and structured Application of the technologies in these and other environments, as well as data collection and data disciplines could help tackle societal “moonshot” processing. These account for roughly half of the challenges.5 For example, researchers at Geisinger activities that people do across all sectors. The have developed an algorithm that could reduce least susceptible categories include managing diagnostic times for intracranial hemorrhaging others, providing expertise, and interfacing by up to 96 percent.6 Researchers at George with stakeholders. Washington University, meanwhile, are using machine learning to more accurately weight the climate models used by the Intergovernmental Panel on Climate Change.7 2 McKinsey Global Institute AI, automation, and the future of work:Ten things to solve for Nearly all occupations will be affected by including rising incomes, increased spending automation, but only about 5 percent of on healthcare, and continuing or stepped- occupations could be fully automated by up investment in infrastructure, energy, and currently demonstrated technologies. Many more technology development and deployment. These occupations have portions of their constituent scenarios showed a range of additional labor activities that are automatable: we find that about demand of between 21 percent to 33 percent of 30 percent of the activities in 60 percent of all the global workforce (555 million and 890 million occupations could be automated. This means that jobs) to 2030, more than offsetting the numbers most workers—from welders to mortgage brokers of jobs lost. Some of the largest gains will be to CEOs—will work alongside rapidly evolving in emerging economies such as India, where machines. The nature of these occupations will the working-age population is already growing likely change as a result. rapidly.11 Jobs lost: Some occupations will see significant Additional economic growth, including from declines by 2030 business dynamism and rising productivity Automation will displace some workers. We growth, will also continue to create jobs. Many have found that around 15 percent of the global other new occupations that we cannot currently workforce, or about 400 million workers, could be imagine will also emerge and may account for as displaced by automation in the period 2016–30. much as 10 percent of jobs created by 2030, if This reflects our mid-point scenario in projecting history is a guide. Moreover, technology itself has the pace and scope of adoption. Under the fastest historically been a net job creator. For example, scenario we have modeled, that figure rises to the introduction of the personal computer in the 30 percent, or 800 million workers. In our slowest 1970s and 1980s created millions of jobs not just adoption scenario, only about 10 million people for semiconductor makers, but also for software would be displaced, close to zero percent of the and app developers of all types, customer service global workforce.10 representatives, and information analysts. The wide range underscores the multiple factors Jobs changed: More jobs than those lost that will impact the pace and scope of AI and or gained will be changed as machines automation adoption. Technical feasibility of complement human labor in the workplace automation is only the first influencing factor. Partial
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