The proliferation of digital information management and communication technologies (IT) in the late twentieth and early twenty-first century has created new opportunities for empirical data collection, data analysis, and communication processes in history and heritage as an academic discipline. In this context of the value of information, artificial intelligence (AI) tools are particularly effective for collecting and analysing data from historical sources. The chapter presents two examples of the application of AI to the study of historical sources: (i) the use of a standard tool (ChatGPT) for empirical data collection from documentary heritage (textual documents, written sources) and (ii) the creation and application of a new AI tool for semi-automatic analysis of the data of tangible (architectural–urban) heritage. Both cases of AI applications are related to a set of limitations. Most of them stem from one central problem—we need as much digital data as possible, but the growing quantity of data (e.g., through crowdsourcing) is difficult to track for authenticity and originality (AI-generated data pretends to be authentic, and transferring digital data makes it untraceable para data- and metadata-wise). The authors propose a solution, based on distributed ledger technology (DLT).