Knowledge Graphs based on ontologies like the CIDOC-CRM provide powerful frameworks for representing archaeological and historical data. However, their creation and querying face significant practical barriers: ontology expertise and knowing the data is required for data modelling, data entry is time-consuming, data transformations are complex, and SPARQL remains inaccessible to most domain scientists. AI offers targeted solutions to each of these challenges. Structured data in the original formats created can be transformed by AI to create the input data for KGs or even created from scans, web pages, or texts using appropriate prompts; ontology and data knowledge can be transferred to AI through sample Knowledge Graph data and mapping examples; AI can write programs for data transformation, validation, and integration into target applications such as databases or GIS; and AI can generate SPARQL queries on behalf of non-technical users. Practical applications at the University of Innsbruck want toshowcase these potentials and suggest an AI assisted pipeline as a discussion facilitator.