AI Due Diligence for Mining

We examine data sources, quality, and integrity. Assessment covers data lineage from source to model, handling of multiple file versions, GIS and spatial data architecture, validation procedures, and governance frameworks. We identify data quality issues that undermine model reliability, including missing values, legacy data problems, and integration challenges that create confusion and risk.

We evaluate how models were validated and whether they actually work as claimed. Assessment includes validation methodology, performance metrics, benchmarking against alternatives, edge case testing, and assumption documentation. We determine if models have been independently validated or simply deployed based on optimistic internal testing, identifying gaps between claimed and actual performance.

We assess whether systems are genuinely production-ready or proof-of-concepts passed off as final products. Evaluation covers production maturity, infrastructure stability, monitoring capabilities, incident response, and rollback procedures. We identify deployment risks including system fragility, inadequate monitoring, poor error handling, and the gap between demonstration environments and operational reality.

We evaluate technical team composition, expertise, and knowledge distribution. Assessment examines AI/ML skills, mining domain knowledge, key person dependencies, staff retention, and cross-functional collaboration. We identify risks from concentrated knowledge, recent departures, hiring without appropriate expertise, and insufficient interaction between data scientists and domain experts who understand geology and mining operations.

We assess how well technical knowledge is captured and transferable. Evaluation covers code documentation practices, system architecture documentation, technical decision records, operational runbooks, and onboarding processes. We determine how quickly knowledge can be transferred to new team members and identify risks from tribal knowledge, inadequate documentation standards, and departed staff taking critical understanding with them.

We examine compliance with mining industry standards and AI governance requirements. Assessment includes JORC/NI 43-101 alignment where AI is used in resource reporting, data protection compliance, model explainability for regulators, and audit trail completeness. We identify regulatory risks from using unvalidated AI in compliance-critical contexts, inadequate documentation for regulatory inspection, and potential liability from non-compliant AI implementations.

We determine if AI systems deliver actual business value or remain theoretical capabilities. Assessment covers operational usage, measurable business impact, user adoption and trust, integration into decision-making processes, and fallback procedures. We identify gaps between claimed ROI and reality, systems built but not used, low user adoption indicating practical problems, and over-reliance on AI without backup processes.

We evaluate long-term viability and maintenance burden of AI systems. Assessment examines code quality, testing coverage, technology currency, dependency management, scalability limitations, and upgrade pathways. We identify accumulating technical debt from rushed implementation, deprecated technologies creating future risk, inadequate testing making changes dangerous, and systems that cannot scale to meet business growth without fundamental rebuilding.