Artificial Intelligence and Machine Learning have brought about a basic reordering of how the archaeologist handles, makes use of and interprets historical data. The discipline is no longer confined to the exacting and slow pace of manual excavation or artifact typology; today it relies on computational models to process the kind of information in volume that LiDAR, photogrammetry and geophysical surveys can yield. In this paper we examine the part AI has played in transforming the field, focusing in particular on the automated classification of finds and algorithmic site discovery. A mixed-methods approach was adopted to cover the institutional as well as the numerical aspects of such an undertaking. We have put together quantitative data from a survey of 850 professionals (field archaeologists, heritage conservators and GIS experts at institutions worldwide) and set it against qualitative material drawn from 60 semi-structured interviews with curators and senior researchers. Our statistical modelling of the former, incorporating reliability testing and hierarchical multiple regression, was meant to determine the predictive worth of AI in terms of analytical efficiency. As for the qualitative transcripts, thematic analysis was applied to reveal systemic impediments, the technical deficit of some traditionalists being one example and the ever-present question of funding another. The findings point to a considerable speeding up of operations. With LiDAR-based detection our models were 94 per cent accurate, something manual approaches could not match. A strong positive correlation between project efficiency and the application of AI is confirmed by the hierarchical regression (β = 0.62, p < 0.001), although model training has been shown to be stymied by a lack of standardised digital archiving (β = -0.45, p < 0.01). In the end, while AI offers unprecedented capabilities, the discipline must undergo structural evolution to address matters of bias and dataset standardisation if it is to make full use of them. We see these results as a framework for the modernising of archaeological practice in the digital age.