AI-driven digital twins for Saudi Arabia's mining sector – gap analysis and roadmap

Purpose A recent systematic literature review revealed zero publications on AI-driven digital twins (AI-DTs) in mining from Saudi Arabia, despite the Kingdom's mineral wealth and Vision 2030 mining investments. This paper addresses this gap by developing a tailored roadmap. Design/methodology/approach We conducted a quantitative gap analysis using an Underrepresentation Index (UI) comparing Saudi Arabia's research output against its mining significance, then mapped Saudi operational realities (extreme heat, dust, remote connectivity, greenfield development) against 68 existing AI-DT studies. Findings China produces 51.5% of AI-DT mining research, while Saudi Arabia has zero publications despite ∼1–2% of global mining output (infinite UI). Existing research mismatches Saudi conditions. We propose a four-pillar roadmap: (1) adapt AI techniques for extreme conditions; (2) establish a national research consortium; (3) sequence three demonstration projects; (4) develop a maturity model (L2 by 2028, L3 by 2030). Originality/value This study provides the first quantitative gap analysis and evidence-informed roadmap for AI-DT adoption in Saudi mining, extending the original SLR from description to prescription.