Technology

Data innovation—the innovative use of data to create social and economic benefits—is making a significant mark in Europe.In economic terms, data innovation contributed about €300 billion to Europe’s economy in 2016 (or approximately 2 percent of GDP), and its value will likely more than double by 2020. Across society, data innovation is creating more responsive governments, better health care, and safer cities. But EU nations differ in the degree to which they are harnessing the benefits of data. This report uses a variety of indicators to rank EU member states and discusses why some countries are ahead and what others can do to catch up.

Governments in Europe and North America want to harness information and communications technology (ICT) to boost productivity and innovation, but uncoordinated strategies and incompatible regulations make it difficult for them to benefit fully from the mutual gains that would come from greater transatlantic cooperation in the development and use of ICT. This report analyzes key policies shaping ICT innovation in Canada, the European Union, and the United States, and identifies opportunities for policymakers to ensure policies maximize productivity and innovation.

The EU’s new data privacy rules, the General Data Protection Regulation (GDPR), will have a negative impact on the development and use of artificial intelligence (AI) in Europe, putting EU firms at a competitive disadvantage compared with their competitors in North America and Asia. The GDPR’s AI-limiting provisions do little to protect consumers, and may, in some cases, even harm them. The EU should reform the GDPR so that these rules do not tie down its digital economy in the coming years.

Cities around the world are undergoing two important transformations. First, they are growing. For the first time in history, a majority of the world’s population lives in urban areas.1 Second, they are beginning to evolve into “smart cities”—cities capable of collecting and analyzing vast quantities of data to automate processes, improve service quality, provide market signal feedback to users, and to make better decisions. While city governments can and should manage much of this transformation, national governments have an important role to play in accelerating and coordinating the development of smart cities. Indeed, the long-term success of smart cities in any particular nation will likely depend on whether the national government supports their development.

The changing nature of work and labor markets — and how best to prepare society and people for the jobs and tasks of the future — is one of the most crucial public policy challenges that countries and policymakers will face over the coming years. While it is far too early to be able to predict the pace and extent of future automation, we do believe that jobs, tasks, and work itself will evolve at a more rapid pace. We also believe that the future of work will affect each country, region, worker, and student differently. For these reasons, this guide seeks to build a bridge from the voluminous future of work research to the core ingredients of future of work policy that will need to be weighed over the coming years. Through our cross-country comparison of future of work dynamics across four case studies — France, Germany, Spain, and the United States — we highlight core factors and key takeaways. We also make the case for more agile public policies that tailor future of work policies to the specificities of countries, regions, and individuals. Ultimately, this guide serves as a resource for policymakers and citizens everywhere who are interested in exploring the essential elements of future of work policy.

Economies are complex production systems with myriad subcomponent production systems—that is, industries—from manufacturing to health care to retail. What and how these production systems produce is grounded in technology. So, as technologies change, production systems change—around the world. Today, the most important and widely shared technologies are digital information technologies that have evolved from the mainframe and mini-computing systems of the 1960s and 70s. They include an array of personal computing devices, back-office servers, IT-embedded machines, and cloud-based services that are connected or dynamically provisioned to users over private networks or the Internet. But the world is now beginning to transform into a new kind of digital system, one that will not only build on existing devices and systems, but also increasingly will incorporate emerging technologies such as sensors, robotics, and artificial intelligence as they improve in price and performance. This next digital economy will be significantly more connected (with many more things, and many more types of things networked, including in more advanced wireless, satellite, and wireline networks), more automated (as devices and systems enable more work to be done by machines), and smarter (as algorithms play increasingly important roles in sensing—and making sense of—all this).

Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the U.S. Defense Advanced Research Projects Agency to spur development of autonomous vehicles.1 In 2011, IBM’s Watson system beat out two longtime human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives. The potential uses of AI to identify patterns, learn from experience, and find novel solutions to new challenges continue to grow as the technology advances. Moreover, AI is already having a major positive impact in many different sectors of the global economy and society. For example, humanitarian organizations are using intelligent chatbots to provide psychological support to Syrian refugees, and doctors are using AI to develop personalized treatments for cancer patients. Unfortunately, the benefits of AI, as well as its likely impact in the years ahead, are vastly underappreciated by policymakers and the public. Moreover, a contrary narrative—that AI raises grave concerns and warrants a precautionary regulatory approach to limit the damages it could cause—has gained prominence, even though it is both wrong and harmful to societal progress. To showcase the overwhelmingly positive impact of AI, this report provides a description of the major uses of AI as well as details on 70 real-world examples of how AI is already generating social and economic benefits. Policymakers should consider these benefits as they evaluate the steps they can take to support the development and adoption of AI.

The time may have finally come for artificial intelligence (AI) after periods of hype followed by several “AI winters” over the past 60 years. AI now powers so many real-world applications, ranging from facial recognition to language translators and assistants like Siri and Alexa, that we barely notice it. Along with these consumer applications, companies across sectors are increasingly harnessing AI’s power in their operations. Embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation. At the same time, AI’s impact on work is likely to be profound. Some occupations as well as demand for some skills will decline, while others grow and many change as people work alongside ever-evolving and increasingly capable machines.
This briefing pulls together various strands of research by the McKinsey Global Institute into AI technologies and their uses, limitations, and impact. It was compiled for the Tallinn Digital Summit that took place in October 2018. The briefing concludes with a set of issues that policy makers and business leaders will need to address to soften the disruptive transitions likely to accompany its adoption.

Experts say the rise of artificial intelligence will make most people better off over the next decade, but many have concerns about how advances in AI will affect what it means to be human, to be productive and to exercise free will.
Digital life is augmenting human capacities and disrupting eons-old human activities. Code-driven systems have spread to more than half of the world’s inhabitants in ambient information and connectivity, offering previously unimagined opportunities and unprecedented threats. As emerging algorithm-driven artificial intelligence (AI) continues to spread, will people be better off than they are today?

The transformation of the European Union’s energy sector poses a number of challenges to the European electricity system. Above all, both the anticipated increase of intermittent electricity from renewable sources and the completion of the internal energy market while guaranteeing a secure supply require an extensive development of electricity infrastructure at the European level. Nevertheless, Europe’s future electricity system entails numerous uncertainties. For example, the definite amount and the location of both electricity generation and consumption are unpredictable. In order to address these ambiguities, the European Network of Transmission System Operators for Electricity (ENTSO-E) develops the two-yearly Ten-Year Network Development Plan (TYNDP). By exploring different plausible future paths using scenarios, the TYNDP aims to identify key infrastructure projects – namely transmission lines and cross-border interconnectors – for the future electricity system. In this capacity, the TYNDP is the central planning tool for European grid infrastructure. This paper explores whether the TYNDP effectively provides a solid planning foundation for future grid investments. For this end, the TYNDPs 2012-2018 are investigated in the light of European energy policies, and their utilization by stakeholders is scrutinized. The paper argues that an existing policy congruence and the strong link to the Projects of Common Interest (PCI) confine the TYNDP solely to the hardware of the electricity system. This in turn profoundly limits the TYNDP’s effectiveness as a strategic planning tool. Hence, a closer connection to the software components of the anticipated European electricity system – namely the future market design and (European) regulations – would further allow for a holistic planning and evaluation of future electricity infrastructure projects.