The Future of Work: A look at the changing nature of work and the challenges and opportunities presented by automation and artificial intelligence 

Accelerating progress in AI and automation is creating opportunities for businesses, the economy, and society ·

While no one knows how artificial intelligence will affect the future of work, we can all agree on one point: AI is one of the world’s most important technologies right now. 

Artificial intelligence is already influencing everything from our search results to our online dating prospects to how we shop. Data show that the use of AI in various commercial areas has increased by 270 percent in the last four years. 

But how will this technology affect work in the future? Will it create a permanent underclass of people who are unable to find work because their jobs have been automated? 

Will super-intelligent computers one day take over the world, rendering the humans who built them obsolete? 

Will robotic servants usher in a golden age of human leisure and prosperity, or will they usher in a period of peace and prosperity? 

According to PwC research, one-third of all jobs will be at risk of being automated by the mid-2030s. Individuals with a low level of education are the most vulnerable workforce segment. 

Automation and artificial intelligence (AI) are transforming businesses and will boost economic growth through productivity gains. They will also contribute to addressing “moonshot” societal challenges ranging from health to climate change. 

Simultaneously, these technologies will change the nature of work and the workplace itself. Machines will be able to do more of the tasks that humans do, supplement the work that humans do, and even perform some tasks that humans cannot. As a result, some occupations will decline, while others will grow, and a variety of others will change. 

While we believe there will be enough work (barring extreme scenarios), society will face significant workforce transitions and dislocation. Workers will need to learn new skills and adapt to the increasingly capable machines that will be working alongside them. 

They may have to transition from declining to growing and, in some cases, new occupations. 

AI and automation progress is accelerating, creating opportunities for businesses, the economy, and society. 

How AI and automation will impact the workplace 

Transitions and challenges in the workforce 

ten problems to solve. 

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AI and automation progress is accelerating, creating opportunities for businesses, the economy, and society. 

Automation and AI are not new concepts, but recent technological advancements are pushing the limits of what machines can do. According to research, society requires these improvements in order to provide value to businesses, contribute to economic growth, and make previously unimaginable progress on some of our most difficult societal challenges. 

Rapid technological advancement 

Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from self-driving cars on the road to automated grocery store checkouts. Much of this advancement has been driven by advancements in systems and components such as mechanics, sensors, and software. AI has made particularly significant advances in recent years, as machine-learning algorithms have become more sophisticated and have taken advantage of massive increases in computing power as well as the exponential growth in data available to train them. Stunning breakthroughs are making headlines, with many involving superhuman capabilities in computer vision, natural language processing, and complex games like Go. 

Possibility of transforming businesses and contributing to economic growth 

These technologies are already adding value to a variety of products and services, and businesses across industries use them in a variety of processes to personalise product recommendations, detect manufacturing anomalies, detect fraudulent transactions, and more. The most recent generation of AI advances, including techniques for classification, estimation, and clustering problems, promises even more value. According to an analysis of several hundred AI use cases, the most advanced deep learning techniques deploying artificial neural networks could account for up to $3.5 trillion to $5.8 trillion in annual value, or 40% of the value created by all analytics techniques. 

The deployment of AI and automation technologies has the potential to significantly boost the global economy and increase global prosperity at a time when ageing and declining birth rates are weighing on growth. Labor productivity growth has slowed in many economies, falling to an average of 0.5 percent 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 this trend: productivity growth could reach 2% per year over the next decade, with digital opportunities accounting for 60% of this increase. 

Possibility of assisting in the resolution of several societal moonshot challenges 

AI is also being used in a variety of fields, including material science, medical research, and climate science. The application of these and other technologies in these and other disciplines could aid in addressing societal moonshot challenges. Geisinger researchers, for example, have developed an algorithm that could cut diagnostic times for intracranial haemorrhaging by up to 96%. Meanwhile, researchers at George Washington University are using machine learning to improve the accuracy of the climate models used by the Intergovernmental Panel on Climate Change. 

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There are still obstacles to overcome before these technologies can fully realise their potential for the benefit of the economy and society everywhere. 

AI and automation continue to face difficulties. The limitations are partly technical in nature, such as the need for massive amounts of training data and the difficulty in “generalising” algorithms across use cases. Recent innovations are only now beginning to address these issues. Other difficulties arise from the application of AI techniques. Explaining decisions made by machine learning algorithms, for example, is technically difficult, which is especially important in use cases involving financial lending or legal applications. Data privacy, malicious use, and security, as well as potential bias in training data and algorithms, must all be addressed. 

Europe is setting the standard with the new General Data Protection Regulation, which codifies more user rights regarding data collection and usage. 

A different type of challenge concerns organisations’ ability to adopt these technologies, which is frequently hampered by people, data availability, technology, and process readiness. Already, adoption is uneven across sectors and countries. The finance, automotive, and telecommunications industries are at the forefront of AI adoption. In 2016, the United States led the way with $15 billion to $23 billion in AI investment, followed by Asia with $8 billion to $12 billion and Europe with $3 billion to $4 billion. 

How AI and automation will impact the workplace 

Even as AI and automation bring benefits to business and society, we must prepare for major disruptions in order to function. 

Approximately half of the activities (not jobs) performed by workers could be automated. 

Our analysis of over 2000 work activities from over 800 occupations reveals that certain types of activities are more easily automated than others. Physical activities in highly predictable and structured environments, as well as data collection and processing, are examples. These activities account for roughly half of all activities performed by people across all sectors. Managing others, providing expertise, and interacting with stakeholders are the least vulnerable categories. 

Automation will affect nearly all occupations, but only about 5% of occupations will be fully automated by currently demonstrated technologies. Many more occupations have portions of their constituent activities that can be automated: we discovered that approximately 30% of the activities in 60% of all occupations could be automated. This means that the majority of workers, from welders to mortgage brokers to CEOs, will have to collaborate with rapidly evolving machines. As a result, the nature of these occupations will most likely change. 

Some occupations will see significant job losses by 2030. 

Some workers will be displaced by automation. We discovered that automation could displace approximately 15% of the global workforce, or approximately 400 million workers, between 2016 and 2030. This corresponds to our midpoint scenario for estimating the pace and scope of adoption. Under our most optimistic scenario, that figure rises to 30%, or 800 million workers. Only about 10 million people would be displaced in our slowest adoption scenario, representing less than 1% of the global workforce. 

The wide range highlights the numerous factors that will influence the pace and scope of AI and automation adoption. The first influencing factor is the technical feasibility of automation. Other considerations include deployment costs, labor-market dynamics such as labor-supply quantity, quality, and associated wages, benefits other than labour substitution that contribute to business cases for adoption, and social norms and acceptance. 

Because of differences in the aforementioned factors, adoption will continue to vary significantly across countries and sectors, particularly labour-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 doubling the rate in India. 

Gained jobs: During the same time period, new jobs will be created. 

Even as workers are displaced, there will be an increase in demand for work and, as a result, jobs. We created labour demand scenarios for 2030 based on several demand drivers, such as rising incomes, increased healthcare spending, and continued or increased investment in infrastructure, energy, and technology development and deployment. These scenarios predicted that additional labour demand would range from 21 percent to 33 percent of the global workforce (555 million to 890 million jobs) by 2030, more than offsetting job losses. Some of the most significant gains will be made in emerging economies such as India, where the working-age population is already rapidly increasing. 

Additional economic growth, such as that resulting from business dynamism and rising productivity growth, will also continue to generate job opportunities. 

Many other new occupations that we cannot currently imagine will emerge, accounting for up to 10% of new jobs created by 2030 if history is any guide. Furthermore, technology has historically been a net job creator. For example, the introduction of the personal computer in the 1970s and 1980s resulted in the creation of millions of jobs not only for semiconductor manufacturers, but also for software and app developers of all types, customer-service representatives, and information analysts. 

Jobs changed: As machines supplement human labour in the workplace, more jobs will be changed than lost or gained. 

As machines supplement human labour, partial automation will become more common. For example, AI algorithms that can read diagnostic scans with high accuracy will assist doctors in diagnosing patient cases and determining appropriate treatment. Jobs with repetitive tasks in other fields may shift towards a model of managing and troubleshooting automated systems. Employees who previously lifted and stacked objects at Amazon are now becoming robot operators, monitoring the automated arms and resolving issues like an interruption in the flow of objects. 

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Things to solve for: 

The most important details in this text are the ten things to solve for the challenges posed by automation and AI. These include ensuring robust economic and productivity growth, fostering business dynamism, investing in human capital, improving labor-market dynamism, and reversing the trend of low public investment in worker training. To address these challenges, companies and governments should harness automation and AI to benefit from the enhanced performance and productivity contributions as well as the societal benefits. To ensure that workforce transitions are as smooth as possible, policy makers should focus on actionable and scalable solutions in several key areas, such as creating a vibrant environment for small businesses, providing simpler and evolved regulations, tax and other incentives, and evolving education systems and learning for a changed workplace. Additionally, digital platforms can help match people with jobs and restore vibrancy to the labor market.  

As more varieties of work and income-earning opportunities emerge, we will need to address issues such as portability of benefits, worker classification, and wage variability. Workflow design and workspace design will need to adapt to a new era in which people work more closely with machines. Organizations are changing too, as work becomes more collaborative and companies seek to become increasingly agile and nonhierarchical. Rethinking incomes, conditional transfers, support for mobility, universal basic income, and adapted social safety nets could be considered and tested. Transition support and safety nets for workers affected will need assistance adjusting. Best practice approaches to transition safety nets are available, and should be adopted and adapted. 

Governments must consider investing in drivers of demand for work, such as infrastructure, climate-change adaptation, and middle-wage jobs. They must also embrace AI and automation safely, taking into account concerns such as data security, privacy, malicious use, and bias. Policy makers, tech and other firms, and individuals must find effective ways to address these issues. 

Conclusion : 

The future of work will require new skills and adaptability of the workforce, so training and retraining both midcareer workers and new generations is essential. Government, private-sector leaders, and innovators need to work together to coordinate public and private initiatives and create incentives to invest in human capital. The future with automation and AI will be challenging, but a richer one if we harness the technologies and mitigate the negative effects. 

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