This chapter characterises the “artificial intelligence (AI) technical landscape”, which has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Since 2011, breakthroughs have taken place in the subset of AI called “machine learning”, in which machines leverage statistical approaches to learn from historical data and make predictions in new situations. The maturity of machine-learning techniques, along with large datasets and increasing computational power are behind the current expansion of AI. This chapter also provides a high-level understanding of an AI system, which predicts, recommends or decides an outcome to influence the environment. In addition, it details a typical AI system lifecycle from i) design, data and models, including planning and design, data collection and processing and model building and interpretation; ii) verification and validation; iii) deployment; to iv) operation and monitoring. Lastly, this chapter proposes a research taxonomy to support policy makers.