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The world of work is being impacted by a fourth industrial revolution, transformed by artificial intelligence and other emerging technologies. With forecasts suggesting large shares of workers, displaced by automation, in need of upskilling/reskilling, the design of active skills policies is necessary. Conventional methods used to anticipate technological change and changing skill needs, such as skill surveys and forecasting, have limited scope to provide insights into emerging trends. With the increasing use of big data and AI methods, analysts have new 'real-time' tools at their disposal. Skill foresight techniques are also increasingly used to gauge in-depth stakeholder information about future technologies and skill needs. A series of short Cedefop guides aims to inform analysts and policy-makers about available skills anticipation methods used to navigate through the uncertainty of changing technologies and skill demands. This first practical guide focuses on conventional skills intelligence methods of surveys and forecasting. [Cedefop wishes to acknowledge the research and services of its contracted institution, the Fondazione Giacomo Brodolini (FGB) (lead partner) in consortium with Economix Research and Consulting, Cambridge Econometrics Ltd, and Panteia B.V. For Cedefop Practical Guide 2, see ED613872. For Cedefop Practical Guide 3, see ED613874.]

The world of work is being impacted by a fourth industrial revolution, transformed by artificial intelligence and other emerging technologies. With forecasts suggesting large shares of workers, displaced by automation, in need of upskilling/reskilling, the design of active skills policies is necessary. Conventional methods used to anticipate technological change and changing skill needs, such as skill surveys and forecasting, have limited scope to provide insights into emerging trends. With the increasing use of big data and AI methods, analysts have new 'real-time' tools at their disposal. Skill foresight techniques are also increasingly used to gauge in-depth stakeholder information about future technologies and skill needs. A series of Cedefop guides aims to inform analysts and policy-makers about available skills anticipation methods used to navigate through the uncertainty of changing technologies and skill demands. This second practical guide focuses on automated skills intelligence methods: big data and AI-driven analyses. [Cedefop wishes to acknowledge the research and services of its contracted institution, the Fondazione Giacomo Brodolini (FGB) (lead partner) in consortium with Economix Research and Consulting, Cambridge Econometrics Ltd, and Panteia B.V. For Cedefop Practical Guide 1, see ED613871. For Cedefop Practical Guide 3, see ED613874.]

The world of work is being impacted by a fourth industrial revolution, transformed by artificial intelligence and other emerging technologies. With forecasts suggesting large shares of workers, displaced by automation, in need of upskilling/reskilling, the design of active skills policies is necessary. Conventional methods used to anticipate technological change and changing skill needs, such as skill surveys and forecasting, have limited scope to provide insights into emerging trends. With the increasing use of big data and AI methods, analysts have new real-time tools at their disposal. Skill foresight techniques are also increasingly used to gauge in-depth stakeholder information about future technologies and skill needs. A series of Cedefop guides aims to inform analysts and policy-makers about available skills anticipation methods used to navigate through the uncertainty of changing technologies and skill demands. This third practical guide focuses on technology and skill foresight methods. [Cedefop wishes to acknowledge the research and services of its contracted institution, the Fondazione Giacomo Brodolini (FGB) (lead partner) in consortium with Economix Research and Consulting, Cambridge Econometrics Ltd, and Panteia B.V. For Cedefop Practical Guide 1, see ED613871. For Cedefop Practical Guide 2, see ED613872.]