The increasing volume of waste from electrical and electronic equipment highlights the need to extend product lifespans through circular economy strategies such as repair, refurbishment, and reuse. Central to these strategies is the ease of disassembly (disassembly index), which directly impact the efficiency of repair and refurbishment processes. France’s repairability index, introduced at the point of sale, provides consumers with transparency on a product’s repair potential. This index is heavily based on disassembly ease and includes four additional factors: spare parts availability and pricing, access to repair documentation, and product-specific criteria. Despite their relevance, calculating the disassembly and repairability indexes is time-consuming and often complex. Artificial Intelligence (AI) offers potential to streamline these evaluations by enabling classification, prediction, and decision-making. In a recent case study, ChatGPT 4.0 was used to calculate these indexes for a capsule coffee machine, leveraging PDF-based index methodologies, product manuals, and online data. To assess accuracy, disassembly times were measured by both experts and beginners-level students during a disassembly workshop in Paris. Video analysis further validated these time measurements. However, findings revealed limitations in AI’s ability to independently and accurately evaluate disassembly and repairability indexes. This highlights the need for a specialized automated system capable of consistently assessing these metrics to support both consumers and circular economy initiatives.