A future that works: automation, employment, and productivity

By J. Manyika, Michael Chui, Mehdi Miremadi, J. Bughin, K. George, Paul Willmott, Martin Dewhurst. 2017

842 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e3ac9558f18234cdf92b730a7386ff16d446a1af

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Citations in the corpus

Exposure of occupations to technologies of the fourth industrial revolution (2021, methodology): “The McKinsey Global Institute Manyika et al. (2017) conducted a detailed evaluation of the automation potential of occupations, in which they identified which sets of capabilities are required for each of over 2000 work activities (O*NET detailed work activities).”
The impact of automation on employment: just the usual structural change? (2018, background): “[52] specified for about 800 occupations, firstly, the activities (out of more than 2000) involved and, secondly, the capabilities (out of 18, divided into “input accepted”, “information processing”, “output delivered” and “physical movement”) required for each activity based on the way humans perfo”
Brave new world: service robots in the frontline (2018, background): “…frontline employees across industries and settings has attracted significant attention from business practitioners (Lelieveld and Wolswinkel, 2017; Manyika et al., 2017; Microsoft, 2018) and recently also from service scholars (Huang and Rust, 2018; Marinova et al., 2017; Čaić et al., 2018; van…”
Robots and Jobs: Evidence from US Labor Markets (2019, background): “Tech. report, McKinsey Global Inst., New York.”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “AI standardisation and rating : Like the ICANN for the internet, there is an urgent need for setting up an independent and transparent Global Alliance for AI Standardisation and Rating which should regulate the AI development, testing and rating system for every AI module or system being created.”
Artificial Intelligence and the Public Sector—Applications and Challenges (2018, background): “…(Hawksworth, Kupelian, Berriman, & Mckellar, 2017), studies of the Oxford University (Frey & Osborne, 2017) and the McKinsey Global Institute (Manyika et al., 2017) reach quite similar conclusions, stating that about half of the occupations or workforce activities could be automated and are…”
The growing ubiquity of algorithms in society: implications, impacts and innovations (2018, background): “Different scenarios are described in [102] depending on relative hardware and software costs,”
What Can Machines Learn, and What Does It Mean for Occupations and the Economy? (2018, background): “A study by the McKinsey Global Institute suggested that about half of the work activities people perform could be automated with current technology ( Manyika et al. 2017 . are impressive While advances in ML , and automation is already having significant effects on many parts of the workforce, we…”