In the mining industry, the terms "Digitalization" and "Automation" are often used interchangeably, but at their core they represent fundamentally different concepts. Digitalization is about transforming mining processes into data: by collecting, quantifying and intepreting every aspect of operations in numercial form. This approach enables a mathematical and visual understanding of how processes function as it provides critical insights into operational dynamics. The focus is on gaining a deeper, data-driven comprehension of the system. In contrast, Automation to a large extent is about substituting human operators with machines or software, reproducing the actions previously performed by them. This can progress towards fully autonomous operations, where systems function without any human intervention or oversight. Thus, in a nutshell, automation - and in its advanced form, autonomy - usually relies on rule-based configurations. These systems are programmed with predefined instructions and parameters, ensuring that processes are executed consistently and predictably. The key value here is the ability to plan and rely on the process outcomes. Automation is ideal for repetitive, well-understood tasks where the primary goal is to remove variability and enhance safety, gain productivity within clearly defined boundarys and achieve cost efficiency. For example, haul trucks following predefined routes. As mining operations become more autonomous, the reliance on human intervention diminishes, but the underlying logic remains rule-based. Digitalization, on the other hand, is about unlocking new levels of understanding. By collecting vast amounts of process data, mining companies can identify inefficiencies, bottlenecks or previously hidden weaknesses. Advanced analytics, artificial intelligence (AI) and machine learning (ML) can then be applied to simulate a large amount of scenarios, predict outcomes and optimize operations - not just replicate existing ones. This continuous feedback loop enables dynamic improvement, rather than static execution. As a result, Digitalization is targeting for a predictive view into the future and improvement. However, usually a human interpretation on the insights provided and active actions on those findings accordingly is a precondition to generate value. While there are significant interdependencies, in particular, how Digitalization and Automation complement each other in modern mining, each one serves a fundamentally different purpose: Digitalization delivers data-driven insights, while automation focuses on replacing human tasks. For this reason, it is important to consider each approach individually, carefully assessing operational needs and addressing their unique challenges. Both are essential to the future of mining. By clearly understanding their distinct roles and benefits to defined mining problems, unlocking the full potential of digital mining transformation can be highly accelerated.