The severe/unacceptable socio-economic and environmental impacts of recent major earthquakes have prompted attention to the crucial need to implement a medium-to-long-term national plan for seismic risk mitigation. The remarkable growth in technology and computation power in recent years is signalling an increased focus toward the goal of creating digital models of the built environment for an enhanced monitoring, control and management. Although the potential advantages of such an approach are evident, there is still a need for adaptive and updatable frameworks and tools for a step-by-step seismic assessment to control, support and drive the data collection process and gain a holistic understanding of the problem prior to conducting often semi-“black-box” numerical simulations. To reach the ambitious goal of seismic risk reduction at national level, a radical improvement of the reliability of the diagnosis-prognosis protocols is required, followed by standardized, low-invasive and cost-effective rehabilitation solutions. In this context, simplified analytical-mechanical procedures can offer fundamental support to the seismic vulnerability assessment process, possibly integrated and powered by artificial-intelligence approaches. One notable example is the SLaMA (Simple Lateral Mechanism Analysis) method, extensively refined and validated in the literature. This method enables the identification of critical structural weaknesses at the component level and the assessment of the overall building capacity, thereby supporting the definition of suitable retrofitting strategies. This paper provides an overview of recent advancements and ongoing continuous refinements of SLaMA-based procedures for seismic risk assessment, loss modelling and retrofit decision-making. Originally conceived for reinforced concrete structures, the SLaMA method has been further improved in recent years and extended to other building material and typologies. Moreover, the mechanical nature of the method makes it effective in accounting for potential local degradation capacity due to either corrosion phenomena or earthquake-related damage. Finally, owing to its inherent simplicity, the procedure can be easily implemented in a knowledgebased risk assessment framework, directly considering uncertainties in building knowledge. The paper will present the potential of SLaMA-based approaches for pre- and post-earthquake safety evaluations, risk and loss assessments, and retrofitting selection.