Public sector has started exploiting artificial intelligence (AI) techniques, however, mainly for operational but much less for tactical or level tasks. The purpose of this study is to exploit AI for the highest strategic-level task of government: to develop an AI-based public sector data analytics methodology for supporting policymaking for one of the most serious and large-scale challenges that governments repeatedly face, the economic crises that lead to economic recessions (though the proposed methodology is of much more general applicability).

A public sector data analytics methodology has been developed, which enables the exploitation of existing public and private sector data, through advanced processing of them using a big data-oriented AI technique, “all-relevant” feature selection, to identify characteristics of firms as well as their external environment that affect (positively or negatively) their resilience to economic crisis.

A first application of the proposed public sector data analytics methodology has been conducted, using Greek firms’ data concerning the economic crisis period 2009–2014, which has led to interesting conclusions and insights, revealing factors affecting the extent of sales revenue decrease in Greek firms during the above crisis period and providing a first validation of the methodology used in this study.

This paper contributes to the advancement of two emerging highly important, for the society, but minimally researched, digital government research domains: public sector data analytics (and especially policy analytics) and government exploitation of AI. It exploits an AI feature selection algorithm, the Boruta “all-relevant” variables identification algorithm, which has been minimally exploited in the past for public sector data analytics, to support the design of public policies for addressing one of the most serious and large-scale economic challenges that governments repeatedly face: the economic crises.

The proposed methodology allows the identification of characteristics of firms as well as their external environment that affect positively or negatively their resilience to economic crisis. This enables a better understanding of the kinds of firms that are more strongly hit by the crisis, which is quite useful for the design of public policies for supporting them; and at the same time reveals firms’ practices, resources, capabilities, etc. that enhance their ability to cope with economic crisis, to design policies for promoting them through educational and support activities.

This methodology can be very useful for the design of more effective public policies for reducing the negative impacts of economic crises on firms, and therefore mitigating their negative consequences for the society, such as unemployment, poverty and social exclusion.

This study develops a novel approach to the exploitation of public and private sector data, based on a minimally exploited, for such purposes, AI technique (“all-relevant” feature selection), to support the design of public policies for addressing one of the most threatening disruptions that modern economies and societies repeatedly face, the economic crises.