Mining businesses must adapt to stay competitive in the face of increasing deposit complexity. Technological advances in automation and machine learning (ML) promise a way forward for smarter resource modelling. These advances deliver more than just efficiency; they represent a paradigm shift in resource estimation (Vespignani and Smyth, 2024). This paper explores how leveraging advanced technologies, cloud-based simulation, and integration with desktop solutions can overcome the inherent limitations of traditional practices, providing unprecedented insights and precision. Historically, the software tools for resource estimation have often functioned in isolation, creating data silos and inefficiencies. Introducing cloud-integrated simulations and fostering seamless platform interoperability and visualisation at end-user workstations can unlock dynamic solutions that better serve geologists, engineers, and decision-makers. In this narrative, we demonstrate how breaking down these silos can enable real-time collaboration, more accurate data reconciliation, and an overall increase in confidence for block model outputs. This paper focuses on the transformative potential of artificial intelligence (Al) and machine learning (ML). By feeding real-time data into dynamic models, these technologies can refine estimates, predict, and adjust for uncertainties in ways that traditional methods, such as conditional simulation, struggle to achieve. The ability to run cloud-based simulations through dynamic cloud-predicted domain models offers a future where resource models become truly adaptive, responding dynamically to changes in input conditions. The authors discuss an automation and software interoperability framework that supports integration across geological, geostatistical, and engineering tools. This approach preserves data integrity while optimising system usability, paving the way for a new era in resource estimation. The paper concludes with strategic recommendations for fostering a culture of innovation and ensuring that industry adoption of these emerging technologies keeps pace with evolving challenges.