During the recent Covid-19 shock (2020/21), most countries used cash transfers to protect the livelihoods of those affected by the pandemic or by restrictions on mobility or economic activities, including the poor and vulnerable. While a large majority of countries mobilized existing programs and/or administrative databases to expand support to new beneficiaries, countries without such programs or databases were severely limited in their capacity to respond. Leveraging the Covid-19 shock as an opportunity to leapfrog and innovate, various low-income countries used new sources of data and computational methods to rapidly develop -level welfare-targeted programs. This paper reviews both crisis-time programs and regular social protection operations to distill lessons that could be applicable for both contexts. It examines three programs from the Democratic Republic of Congo, Togo, and Nigeria that used geospatial and mobile phone usage data and/or artificial intelligence (AI), particularly machine learning methods to estimate the welfare of applicants for individual-level welfare targeting and deliver emergency cash transfers in response to the pandemic. Additionally, it reviews two post-pandemic programs, in Lomé, Togo and in rural Lilongwe, Malawi, that incorporated those innovations into the more traditional delivery infrastructure and expanded their monitoring and evaluation framework. The rationale, key achievements, and main challenges of the various approaches are considered, and cases from other countries, as well as innovations beyond targeting, are taken into account. The paper concludes with policy recommendations and promising research topics to inform the discourse on leveraging novel data sources and estimation methods for improved social assistance in and beyond emergency settings
