Global Talent in the Age of Artificial Intelligence

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Addressing the theme of Global Talent in the Age of Artificial Intelligence, this seventh edition of GTCI explores how the development of Artificial Intelligence (AI) is changing the nature of work and forcing a re-evaluation of workplace practices, corporate structures and innovation ecosystems. As machines and algorithms continue to affect a multiplicity of tasks and responsibilities and almost every job gets reinvented, the right talent is required not only to carry out new responsibilities and ways to work, but also to capture value from this transformative technology. Which companies, countries or cities are best positioned to benefit from the AI revolution? How can we guarantee that a joint effort be made to ensure that AI-driven increased productivity benefits society as a whole?

January 20, 2020

GTCI is an annual benchmarking report that measures and ranks countries based on their ability to grow, attract and retain talent. Launched for the first time in 2014, the GTCI provides a wealth of data and analysis that helps decision makers develop talent strategies, overcome talent mismatches and become more competitive in the global marketplace.

This year again, the GTCI model has been refined and improved. Some variables have been removed or replaced and a few new ones have been added. One of the main new features is the introduction of a ‘Technology adoption’ component that provides a measure of how countries use and invest in new technologies, including AI. As a result, the total number of indicators has increased from 68 to 70. Country coverage in the GTCI 2020 has also expanded and the index now includes 132 countries — up from last year’s 125 countries.

Now a regular feature of the report, the special section on cities offers a ranking of 155 cities along the various dimensions of the Global City Talent Competitiveness Index (GCTCI). This year, the model has primarily been improved in three ways: First, variables that are more business- and impact-oriented — for example, those on foreign direct investment and patent applications—have been introduced to the model. Second, the share of values proxied by regional or national data has been reduced, so that almost all values refer to city-level data. Third, the structure of the model has been refined in that some indicators have been placed in another pillar for conceptual reasons, which has also resulted in bringing the GCTCI model closer to the GTCI model.

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