Digisation and the continuous advancement of Artificial Intelligence (AI) incites projections about automation altering occupations over many sectors and countries. With increasing availability of digital infrastructure, many parts of emerging economies will open their doors and become subject to digitisation, robots and AI simultaneously (Messina 2016, Das and Hilgenstock 2018), which for advanced economies have appeared slower, with less established power structures and at different times. With high uncertainty around and little literature on the subject, the societal implications of automation in emerging economies are ambiguous, with potentials of automation boosting economies and leveling the playing field of development one one hand, but also leading to exploitative dynamics, in which companies with intellectual property rights and know-how on automation technologies inflict harm on labour markets, economies and thus many societal layers of emerging economies. While a multitude of indicators on job transformation through digitisation, robots or AI and their respective implications on employment emerged and were discussed within the literature for advanced economies, lack of abundant data causes ongoing uncertainty for emerging economies. This thesis addresses the scientific gap of who is at risk of automation in emerging economies. The literature review entails an overview on the field of labour automation, the social effects of automation, and the (projected) case for emerging economies. The quantitative analysis entails the calculation of two automation indicators, Routine Task Intensity for automation through digitisation and robots, and Suitability for Machine Learning (SML) for automation through AI for emerging economies. SML and RTI are compared among occupations and socio-demographic groups as well as their relationship towards each other. To identify patterns to susceptible socio-demographic groups within the labour markets of emerging economies, a cluster analysis is performed on the most pertinent and yet orthogonal demographic parameters. The attained insights on socio-demographic clusters and their susceptibility towards automation are discussed to develop are broader picture on how automation affects emerging economies in order to educate policymakers and aid in rethinking policy approaches in response to changes in skill demands. This thesis finds ground for concern that emerging economies will be subject to a not just broader but also likely more sudden wave of digital automation than advanced economies, potentially magnified through historic power imbalances and global market dynamics. Our results demonstrate that also for the task compositions of emerging economies, it can be expected that with the advent of AI a substantively larger share of tasks, occupations and socio-demographic groups is susceptible to automation as compared to automation through digitisation and robots. Workers with low and high education show higher automation susceptibilities than mid-educated workers, although through different technologies. The finding that also high-educated workers are susceptible motivates the conclusion that education systems might see their traditional societal role of enablers of social mobility endangered. Finally, this thesis finds that women are systematically more susceptible to AI-enabled automation and the observation that men are more susceptible to automation through digitisation and robots in advanced economies does not hold true for emerging economies. This thesis concludes by calling for particular consideration of emerging economies in the field of automation in labour markets, the merging of more granular data for emerging economies and an institutional model of automation.