Mass digitization and the expansion of born-digital archives have multiplied the availability of historical sources, yet effective reuse remains constrained by fragmentation across formats, metadata schemas, OCR conditions, and institutional infrastructures. Rather than a mere shortage of tools, this “Babel” condition reflects an infrastructural and epistemological fragmentation of historical workflows. Interoperability cannot be assumed as an automatic by-product of digitization; it must be designed as a governable, traceable workflow condition. This short paper conceptualizes AI not as an autonomous analytical engine, but as a human-governed mediation layer operating between heterogeneous source ecosystems, standards, and research practices. AI is positioned as an orchestration mechanism that assists transitions between workflow phases, particularly where friction arises between formats, schemas, and languages, while preserving source criticism, tool criticism, and expert validation. A modular six-step workflow is proposed: heterogeneous ingestion; OCR/HTR retrieval and correction; normalization; semantic enrichment; remodeling into interoperable structures (CSV/JSON/graph-ready outputs); and analytical production (networks, timelines, geodata). The focus is methodological rather than technical, emphasizing interoperability bottlenecks such as metadata normalization and schema mediation.

The argument is demonstrated through a comparative proof-of-concept on two historical serial corpora: French Red Cross bulletins hosted on Gallica and researcher-digitized Italian Red Cross bulletins. Evaluation adopts workflow-oriented criteria—time-to-integration, reduction of manual mediation, adaptability, traceability, and qualitative error taxonomy. The paper proposes AI-mediated interoperability as a reusable, inspectable, and methodologically accountable strategy for historically grounded Digital Humanities research.