This article presents the findings of an experiment in the use of Artificial Intelligence text generation processes to convert historical 'big data' into narrative text. Using an extensive collection of records pertaining to the Australian colonial settlement of Norfolk Island in the South Pacific (1788–1814), we investigate Generative Large Language Model technology for converting tabulated data from the site into short pieces of novel text, describing the lives of transported convicts and free individuals recorded in our databases. These personalized stories are assessed for fluency and factual correctness. Using this process, we uncover some instructive problems and caveats. We detect AI's inherent tendency toward bias and uncritical perspectives, including potentially offensive stereotypes. We also discover an unwelcome tendency to summarize data. So, whilst the outputs are for the most part effective and functional, we find that the best results still require artful human intervention to fully capture the most human aspects of history and heritage research.