The application of machine learning (ML) and artificial intelligence (AI) is gaining momentum in the mining industry. These technologies are now widely used to develop digital twins, optimise ore sorting and energy usage, enable predictive maintenance, and analyse large data sets for exploration and modelling. Companies such as Earth.AI, StratumAI, and OreFox are leveraging AI and neural networks to process geochemical, geophysical, and drill hole data, aiming for a first mover advantage in exploration and targeting. Meanwhile, software providers like Micromine (Grade Copilot) and Maptek (DomainMCF) have introduced AI-powered modules that enable users to model numerical and categorical data directly. These tools are increasingly being used to construct geological and grade models that feed into Mineral Resource estimates. However, a critical shortcoming remains: the absence of geological chronology. Geological features such as lithology, alteration, and oxidation do not simply co-exist in space. A sequence of geological events governs their distribution. Most AI-assisted tools currently model spatial relationships without accounting for the temporal order of geological processes. This often results in models that appear realistic at a global scale but fail to honour geological constraints when examined in detail. This paper proposes a methodology that combines the analytical strengths of AI with the interpretive value of geological chronology. By integrating event sequencing into the modelling workflow with several examples, we demonstrate how AI can be used not just to generate plausible models, but to construct geologically valid interpretations that respect the timing and relationships between geological units. This approach improves the robustness of geological models and the reliability of the domains used in resource estimation.