Innovation

Notwithstanding the emergence of artificial intelligence (AI), robotics, and the Internet of Things (IOT), European productivity growth has slowed, and continues to lag U.S. growth.1 Since the financial crisis, labor productivity in the 28 EU member states has grown just 0.7 percent annually. At this rate, it will take a century for Europe’s per capita incomes to double. No wonder there is political unrest across the continent. And while Europe decreased the productivity gap with the United States before 1995, since then, the gap has only widened. Reversing that trend is critical if Europe is going to be able to effectively cope with its demographic challenges, particularly a rapidly aging population, and be able to more effectively compete in global markets. To do that it needs more ubiquitous use—as distinct from production—of information and communication technologies (ICTs) by all organizations (for-profit, nonprofit, and government) throughout all of Europe.

Data-driven innovation is unlocking new opportunities for Europe to grow its economy and address pressing social challenges. While Europe has achieved some early successes in data-driven innovation, including in areas such as education, energy, environmental management, health care, open data, smart cities, and smart manufacturing, it has not yet come close to reaching its full potential. The primary obstacle is that Europe’s policymakers, both in its capital cities and in Brussels, have not yet fully embraced data-driven innovation as a core driver of economic and social progress. To inject new leadership into this debate, Member States should appoint national chief data officers to not only champion data innovation domestically, but also serve on a new, independent advisory panel charged with counseling the European Commission on how to seize opportunities to innovate with data.

Data-driven innovations have the power to address some of the most pressing social challenges in Europe. While many government and non-governmental organizations (NGOs) are using data in their attempts to tackle a range of social issues from high unemployment to the refugee crisis, more can be done. To accelerate progress, public and private-sector leaders should take steps to collect data on disadvantaged populations, facilitate cross-sector collaboration on data projects for social good, and implement policies that encourage data use, reuse, and sharing in support of social goals.

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.