The Rise of Automation and AI in the Workplace
The rapid advancements in automation and artificial intelligence (AI) are transforming businesses and industries worldwide, contributing to economic growth through increased productivity. These transformative technologies are not only streamlining traditional industrial processes but also tackling complex societal challenges, from healthcare to climate change. As machines become increasingly capable of performing tasks that were once the exclusive domain of humans, the nature of work and the workplace itself is undergoing a significant shift.
Machines are now able to carry out a growing number of tasks, complementing and even surpassing human capabilities in certain areas. This transition is driving changes in the job market, with some occupations declining, others growing, and many more undergoing significant transformations. While there may be enough work to go around, barring extreme scenarios, society will need to navigate through significant workforce transitions and dislocation. Workers will be required to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace, potentially moving from declining occupations to growing or even entirely new ones.
The Sensor-Powered Revolution in Robotics
At the heart of this technological revolution are advancements in sensor networks, IoT, and related technologies. Improvements in sensors, mechanics, and software, combined with the exponential growth in data and computing power, have propelled the development of more autonomous systems than ever before. These systems are now appearing in a vast array of environments, from autonomous vehicles on roads to automated check-outs in grocery stores.
One of the key drivers behind this sensor-powered revolution is the progress in AI, particularly in machine learning algorithms. As these algorithms have become more sophisticated and have been able to leverage the abundance of data and computing resources, they have enabled beyond-human capabilities in areas such as computer vision, natural language processing, and complex problem-solving tasks like the game of Go.
These AI-powered autonomous systems are already generating significant value across various industries and applications. Companies are utilizing them to personalize product recommendations, detect anomalies in production, identify fraudulent transactions, and much more. The latest advancements in deep learning techniques have the potential to account for up to $58 trillion in annual value, or 40% of the value created by all analytics techniques.
The Promise and Challenges of Sensor-Powered Robotics
The widespread deployment of automation and AI technologies holds immense promise for lifting the global economy and increasing global prosperity. This is particularly crucial at a time when factors such as aging populations and declining birth rates are acting as a drag on economic growth. By boosting labor productivity, a key driver of economic growth, these technologies have the potential to reverse the slow-down observed in many economies in recent years.
Moreover, the application of AI and automation in areas ranging from material science to medical research and climate science could help tackle some of the most pressing societal moonshot challenges facing humanity. For instance, researchers have developed algorithms that can reduce diagnostic times for intracranial hemorrhaging by up to 96%, and others are using machine learning to more accurately model the climate systems used by the Intergovernmental Panel on Climate Change.
However, these technologies also face significant challenges, both technical and practical. On the technical side, issues such as the need for massive training data and the difficulty of generalizing algorithms across use cases remain. Additionally, explaining the decisions made by machine learning algorithms is a challenging task, which is particularly crucial in applications involving financial lending or legal decisions.
Other practical challenges include potential bias in the training data and algorithms, as well as concerns around data privacy and security. These issues must be addressed to ensure the responsible and ethical deployment of these technologies. Regulatory frameworks, such as the General Data Protection Regulation (GDPR) in Europe, are starting to codify more rights for users over data collection and usage.
Workforce Transitions and the Future of Work
Perhaps the most significant challenge posed by the rise of automation and AI is the impact on the workforce. Our analysis of more than 2,000 work activities across over 800 occupations shows that certain categories of tasks, such as physical activities in highly predictable environments and data collection and processing, are more easily automatable than others, such as managing others, providing expertise, and interfacing with stakeholders.
While only about 5% of occupations could be fully automated by currently demonstrated technologies, a much larger proportion of the activities within most occupations are automatable, with around 30% of the activities in 60% of all occupations potentially being automated. This means that most workers – from welders to mortgage brokers to CEOs – will be working alongside rapidly evolving machines, and the nature of their occupations is likely to change as a result.
Automation will inevitably lead to the displacement of some workers. Our research suggests that around 15% of the global workforce, or about 400 million workers, could be displaced by automation in the period 2016-2030 in a midpoint scenario. This figure could rise to 30%, or 800 million workers, in the fastest adoption scenario.
However, it is important to note that the pace and scope of automation adoption will vary significantly across countries and sectors, depending on factors such as the technical feasibility of automation, the cost of deployment, labor-market dynamics, and social norms and acceptance.
The Upside: Growth in Demand for New Work and Jobs
While automation and AI will displace some workers, there will also be significant growth in demand for new work and jobs. Our scenarios for labor demand to 2030 suggest an additional labor demand of between 21% to 33% of the global workforce, or 555 million to 890 million new jobs, more than offsetting the numbers of jobs lost.
Some of the largest gains in job demand are expected to occur in emerging economies, such as India, where the working-age population is already growing rapidly. Additionally, economic growth, including from business dynamism and rising productivity, will continue to create new jobs.
Moreover, technology itself has historically been a net job creator. For example, the introduction of the personal computer in the 1970s and 1980s led to the creation of millions of jobs, not just for semiconductor makers but also for software and app developers, customer-service representatives, and information analysts.
As partial automation becomes more prevalent, many jobs will shift towards a model of managing and troubleshooting automated systems, rather than performing the tasks themselves. For instance, in the retail sector, employees who previously lifted and stacked objects are now becoming robot operators, monitoring the automated arms and resolving any issues.
The Changing Landscape of Skills and Occupations
While there may be enough work to ensure full employment in 2030, the workforce transitions accompanying the adoption of automation and AI will be significant. The mix of occupations will change, as will the skill and educational requirements for workers.
Automation will accelerate the shift in required workforce skills that we have seen over the past 15 years. Demand for advanced technological skills, such as programming, will grow rapidly, as will the demand for social, emotional, and higher cognitive skills, such as creativity, critical thinking, and complex information processing.
Conversely, the demand for physical and manual skills will decline, although they will remain the single largest category of workforce skills in 2030 in many countries. This shift in skill requirements will put additional pressure on the already existing workforce-skills challenge, as well as the need for new credentialing systems to keep pace with the changing job market.
Our research suggests that in a midpoint scenario, around 3% of the global workforce will need to change occupational categories by 2030, with the range varying from about 0% to 14%. These shifts will occur not only within companies and sectors but also across sectors and even geographies.
Occupations dominated by physical activities in highly structured environments or data processing and collection are likely to see declines, while growing occupations will include those with difficult-to-automate activities, such as managers, and those in unpredictable physical environments, such as plumbers. Demand for work is also expected to increase in occupations such as teachers, nursing aides, and various tech and other professional roles.
The Future of Work: Designing Workflows and Workspaces
As intelligent machines and software become more deeply integrated into the workplace, workflows and workspaces will continue to evolve to enable humans and machines to work together effectively.
For example, as self-checkout machines are introduced in stores, cashiers can transition into checkout assistance helpers, who can help answer questions or troubleshoot the machines. More system-level solutions will prompt the rethinking of entire workflows and workspaces, such as warehouse design, which may change significantly to accommodate both robots and humans in a safe and productive manner.
These occupational mix shifts are likely to put pressure on wages, as many of the current middle-wage jobs in advanced economies are dominated by highly automatable activities, such as in manufacturing or accounting, which are likely to decline. High-wage jobs will grow significantly, especially for high-skill medical and tech professionals, but a large portion of the expected new jobs, 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.
Preparing for the Future: Collaborative Solutions
The challenges posed by the rise of automation and AI are significant, but they are not insurmountable. Rather than seeking to roll back or slow the diffusion of these transformative technologies, the focus should be on ensuring that the workforce transitions are as smooth as possible.
This will require actionable and scalable solutions developed through the collaborative efforts of government, private-sector leaders, and innovators. Key areas that need to be addressed include:
- Workforce Training and Retraining: Investing in the education and training of both midcareer workers and new generations to equip them with the skills necessary for the future of work.
- Incentives for Human Capital Investment: Creating the right incentives for companies and individuals to invest more in human capital development.
- Coordinating Public and Private Initiatives: Better coordinating public and private initiatives to address the workforce-skills challenge and support worker transition and dislocation assistance.
While the future with automation and AI will undoubtedly be challenging, it also holds the promise of a much richer and more prosperous world. By harnessing these technologies with aplomb and mitigating the negative effects, we can ensure that there will be work for everyone in the years to come, even if the nature of that work is dramatically different.
Sensor-Networks.org is committed to exploring the latest advancements in sensor networks, IoT, and related technologies, and how they are shaping the future of work and industry. Stay tuned for more in-depth analysis and practical insights on this rapidly evolving landscape.