Trends Identified
New materials and biotech
New materials and biotech, which include advanced materials, such as new lightweight materials, and next-generation genomics.
2019
Tech for good
McKinsey
Clean tech
Clean tech, which primarily consists of renewable energy sources such as sun and wind energy, supported by devices for energy storage.
2019
Tech for good
McKinsey
The pace of technological progress is accelerating, bringing significant opportunities to create value even as it redefines the future of work
Digital technologies have been reinventing the way we live, work, and organize. Smartphones, the mobile internet, e-commerce, and cloud-based services have opened the door to more mobility and convenience as well as to greater competition. Businesses have been harnessing advanced analytics and the Internet of Things to transform their operations, and those in the forefront reap the benefits: companies that are digital leaders in their sectors have faster revenue growth and higher productivity than their less-digitized peers. They improve profit margins three times more rapidly than average and are often the fastest innovators and the disruptors of their sectors. The forces of digital have yet to become fully mainstream, however. On average, industries are less than 40 percent digitized, despite the relatively deep penetration of these technologies in media, retail, and high tech. Now comes the next wave of innovation, in the form of advanced automation and artificial intelligence (AI). An explosion in algorithmic capabilities, computing capacity, and data is enabling beyond-human machine competencies and a new generation of systemlevel innovation. Machines already surpass human performance in areas like image recognition and object detection, and these capabilities can be used to diagnose skin cancer or lip-read more accurately than human experts. Combining these capabilities is leading to system-level innovation, for example the driverless car, which takes advantage of innovations in sensors, LIDAR, machine vision, mapping, satellites, navigation algorithms, and robotics. Our research finds that companies in the forefront of adopting AI are likely to increase employment rather than reduce it, as innovationfocused adopters position themselves for growth, which tends to stimulate employment. These technologies still have limitations, and deployment can be complex. Nonetheless, productivity gains across sectors are already visible, with AI use cases in functions such as sales and marketing (e.g., “next product to buy” personalization), supply chain and logistics, and preventive maintenance. Our analysis of more than 400 use cases across 19 industries and nine business functions found that AI could improve on traditional analytics techniques in 69 percent of potential use cases. Deep learning could account 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 3). For the global economy, too, AI adoption could be a boon. A simulation we conducted showed that AI adoption could raise global GDP by as much as $13 trillion by 2030, or about 1.2 percent additional GDP growth per year. AI could also contribute to tackling pressing societal challenges, from healthcare to climate change to humanitarian crises; a library of social good use cases we collected maps to all 17 of the UN’s Sustainable Development Goals. Yet AI is not a silver bullet. Significant bottlenecks, especially relating to data accessibility and talent, will need to be overcome, and AI presents risks that will need to be mitigated. It could introduce or exacerbate social challenges, for example through malicious use or abuse, bias, privacy invasion, or lack of transparency.
2019
Navigating a world of disruption
McKinsey
Live and kicking
‘Live’ moments are very much alive and kicking, as technology brings us together to share our experiences in real time.
2019
Trends 2019
Mindshare
Look who’s talking
The rapid adoption of technology and the spoken word to enrich our lives.
2019
Trends 2019
Mindshare
Seconds saved
Tech helping our lives run more smoothly, saving us seconds on every-day tasks.
2019
Trends 2019
Mindshare
Real or replica?
In a world of growing mistrust, where tech is presenting us with curated social media, fake videos, voice assistants and virtual entities, people are re-evaluating who is real and who is not.
2019
Trends 2019
Mindshare
Aging populations are forcing developed regions worldwide to rely more on waning productivity and greater migration to propel growth
Labor productivity growth has waned and is near historic lows in the United States and much of Western Europe, despite a job-rich recovery after the global financial crisis. Productivity growth averaged just 0.5 percent in 2010–14, down from 2.4 percent a decade earlier. This productivity growth weakness comes as birth rates in countries from Germany, Japan, and South Korea to China and Russia are far below replacement rates and working-age population growth has either slowed or gone into reverse. In some countries with declining populations, such as Japan and Germany, some cities are shrinking. Among their other effects, these demographic trends put a greater onus on productivity growth to propel GDP growth; over the past 50 years, just under half of GDP growth in G-20 countries came from labor force growth, while productivity growth accounted for the remainder. Digitization, often involving a transformation of operating and business models, promises significant productivityboosting opportunities in the future, but the benefits have not yet materialized at scale in productivity data because of adoption barriers and lag effects as well as transition costs. Our research suggests that productivity could grow by at least 2 percent annually over the next 10 years, with 60 percent coming from digital opportunities. However, while crisis-related aftereffects are diminishing, long-term drags on demand for goods and services may persist and hold back productivity, a result of changing demographics, declining labor share of income, rising income inequality, polarization of labor markets, and falling investment rates. In terms of consumption, the aging population in many developed countries (that is, the retired and elderly over 60) are increasingly important drivers of global consumption. The number of people in this age group will grow by more than one-third, from 164 million today to 222 million in 2030. We estimate that they will generate 51 percent of urban consumption growth in developed countries, or $4.4 trillion, in the period to 2030. That is 19 percent of global consumption growth. The 75-plus age group’s urban consumption is projected to grow at a compound annual rate of 4.5 percent between 2015 and 2030. In addition to increasing in number, individuals in this group are consuming more, on average, than younger consumers, mostly because of rising public and private healthcare expenditure. Retirees and the elderly in developed economies today have per capita consumption of around $39,000 per year. In comparison, the 30-to-44 age group consumes on average $29,500 per year. Healthcare spending by those aged 60 and older is projected to grow by $1.4 trillion in the period to 2030. With low fertility in the developed world, migration has become the primary driver of population and labor force growth in key developed regions worldwide. Since 2000, growth in the total number of migrants in developed countries has averaged 3.0 percent annually, far outstripping the 0.6 percent annual population growth in these nations. First-generation immigrants constitute 13 percent of the population in Western Europe, 15 percent of the population in North America, and 48 percent in the Gulf Cooperation Council countries. Besides contributing to output today, immigrants provide a needed demographic boost to the current and future labor force in destination countries. Improving the old-age dependency ratio is of critical importance to countries like Germany, Spain, Canada, and the United Kingdom, where most public pensions have a pay-as-you-go structure and worsening dependency ratios threaten to make many plans unsustainable.
2019
Navigating a world of disruption
McKinsey
Robot dexterity
Robots are teaching themselves to handle the physical world.For all the talk about machines taking jobs, industrial robots are still clumsy and inflexible. A robot can repeatedly pick up a component on an assembly line with amazing precision and without ever getting bored—but move the object half an inch, or replace it with something slightly different, and the machine will fumble ineptly or paw at thin air. But while a robot can’t yet be programmed to figure out how to grasp any object just by looking at it, as people do, it can now learn to manipulate the object on its own through virtual trial and error. One such project is Dactyl, a robot that taught itself to flip a toy building block in its fingers. Dactyl, which comes from the San Francisco nonprofit OpenAI, consists of an off-the-shelf robot hand surrounded by an array of lights and cameras. Using what’s known as reinforcement learning, neural-network software learns how to grasp and turn the block within a simulated environment before the hand tries it out for real. The software experiments, randomly at first, strengthening connections within the network over time as it gets closer to its goal. It usually isn’t possible to transfer that type of virtual practice to the real world, because things like friction or the varied properties of different materials are so difficult to simulate. The OpenAI team got around this by adding randomness to the virtual training, giving the robot a proxy for the messiness of reality. We’ll need further breakthroughs for robots to master the advanced dexterity needed in a real warehouse or factory. But if researchers can reliably employ this kind of learning, robots might eventually assemble our gadgets, load our dishwashers, and even help Grandma out of bed. —Will Knight
2019
10 Breakthrough Technologies 2019 - How we’ll invent the future, by Bill Gates
MIT Technology Review