Abstract Purpose This research aims to develop a critical understanding of the employment of digital technologies (DTs) for LCA studies, outlining both the opportunities and challenges associated with circular strategies. Two research questions are thus addressed: (1) What circular loops and aspects are addressed when digital technologies are integrated in the development of a Life Cycle Inventory? (2) Which trade-offs are revealed in the integration of digital technologies in Life Cycle Inventory development addressing circularity along a life cycle? Methods This study is based on the problematisation approach, which critically examines existing assumptions in the LCA literature, structured into six principles: defining a domain of investigation, articulating and evaluating assumptions, developing alternative perspectives, involving the audience through qualitative interviews, and assessing the alternative assumptions. A systematic literature review (SLR) and semi-structured interviews with experts were conducted to explore these issues and suggest future research directions. Results and discussion It emerges that the DTs are mainly integrated in the Life Cycle Inventory phase capturing closing and narrowing loops, whereas a limited number of cases investigate slowing loops with different aspects investigated. However, even if DTs can facilitate and improve the trustworthiness of the inventory, they can also lead to an increase in complexity because more competencies are needed, it is more difficult to control data collection and elaboration, and more social interactions along the supply chain are needed. At the same time, DTs can reduce flexibility because further improvements are blocked, interfaces can be rigid to connect, and technical and normative updates can be more difficult to implement. Conclusions DTs improve the development of the Life Cycle Inventory phase, particularly in the context of the circular economy. However, they also introduce new complexities and challenges. The use of blockchain, digital twins, and IoT sensors, for instance, has significantly improved data transparency and traceability, which are critical for circular economy practices, but complexity and training requirements can limit their benefits, so careful consideration must be given to their implementation to maximise benefits and minimise drawbacks.