FAIR principles have become reference criteria for promoting and evaluating openness of scientific data and for improving datasets Findability, Accessibility, Interoperability and Reusability. This also applies to Research Infrastructures (RIs) in the solid Earth domain committed to provide access to seismological data, ground deformations inferred from terrestrial and satellite observations, geological maps and laboratory experiments. Such RIs are indeed committed since a long time, well before the appearance of FAIR principles, to engage scientific communities involved in data collection, standardization and quality control as well as in implementing metadata and services for qualification, storage and accessibility. By addressing open science and managing scientific data, they are working to adopt FAIR principles, thus having the onerous task of turning these principles into practices. In this work we argue that although FAIR principles have the merit of creating a common background of knowledge to engage communities in providing data in a standard way thus easing interoperability and data sharing, in order to make the adoption of FAIR principles less onerous there is an urgent need of clear models, reference architectures and technical guidelines which can support RI implementers in the realization of FAIR data provision systems. We therefore discuss the state of the art of FAIR principles ecosystem and open new perspectives by discussing a four-stages roadmap that reorganizes FAIR principles in a way that better fits to the approach of RI implementers, and a FAIR adoption process that relates FAIR principles to technologies for their implementation.