While South Africa’s mining sector demonstrates “pockets of excellence” in digitalisation and AI, its overall maturity remains uneven, as many programmes still stall at pilot stage because of data fragmentation and limited end-to-end integration, says management consultancy Boston Consulting Group (BCG).

Globally, BCG’s digital acceleration index has shown that the metals and mining industries are about 30% to 40% less digitally mature than comparable industries; therefore, the “opportunity to catch up is material”, says BCG partner Puso Thahane.

BCG assesses digital maturity using a structured framework that aims to look beyond the “technology deployed to value delivered at scale” across strategic vision and operating models, data and technology foundations, the portfolio of digital and AI products being built, and the organisation’s ability to scale through governance, capability building and change management.

“The goal of this approach is to move companies from ‘pilot-purgatory’ to future-fit, AI-enabled operations with measurable outcomes in safety, reliability, throughput and cost,” he adds.

BCG’s experience – through its work with mining companies across Africa – shows that digital leaders can achieve step-change operational outcomes, including throughput improvements of about 10% to 20%.

In addition, when deployed at scale, integrated Generative AI (GenAI) and predictive maintenance for mining fleets can increase availability by about 15% within six months, while reducing costs by about 10% when embedded end-to-end across systems and workflows.

Targeting Bottlenecks

Returns can come faster than many leaders expect, especially when digital and AI-driven mining is targeted at operational bottlenecks and not deployed as “technology for technology’s sake” by embedding it into day-to-day workflows, explains BCG principal information technology architect Akash Singh.

Tangibly, four operational levers – end-to-end planning and scheduling, workforce capability and skills development, maintenance execution using GenAI, and data and advanced analytics – play a critical role across two key themes, decision-making support and work execution support, to unlock business value for AI in asset-intensive industries.

He notes that AI programmes create outsized impact at scale when they strike the right balance between decision support and execution support, with maintenance being “a clear case in point”.

“In many operations, unplanned work can account for about 60% of total maintenance spend, driven by breakdowns, reactive scheduling, and suboptimal parts and labour deployment,” states Singh.

Further, when GenAI is paired with workflow orchestration – to translate insights into work orders, optimise job plans, coordinate crews and materials, and close the loop through structured feedback – even modest performance gains can compound quickly, he adds.

Additionally, a 5% to 15% uplift in equipment availability, for example, can translate into substantial value when that additional availability is converted into incremental production time, improving throughput without proportional increases in cost or capital, says Singh.

“Through our experience, we’ve learned that digital value in mining and other asset-intensive industries does not come from dashboards, it comes when better decisions are enabled consistently for every shift, supported by the right data,” he states.

Integrating AI

The next phase of digital and AI-driven mining will be defined by integrated AI-enabled operating platforms that move away from isolated dashboards and pilots to systems that can sense, decide and coordinate action across maintenance, production, planning and safety, says Thahane.

These systems make two shifts visible: one from prescriptive maintenance and predictive insights to autonomous coordination – such as diagnosing issues, triggering work, ordering parts and scheduling interventions – and the other from standalone algorithms and GenAI co-pilots to agentic AI in selected workflows. In the latter, systems can pursue goals and take bounded actions in real time, with human oversight, he adds.

As for mining companies and mining operations in Africa, BCG believes that the competitive advantage from digital and AI-driven mining over the next decade will stem from acting on three fundamentals: using data analytics to build digital solutions in service of step changes in operational performance, designing an enterprise platform approach and investing in workforce capability and governance.

Here, Thahane notes that adequate data foundations are tied to operational value, while the design of an enterprise platform approach enables the scaling of solutions across assets and operating sites.

“Those who move early can leapfrog – embedding intelligence across operations, supply chains and safety systems, while those who hesitate risk being outpaced as AI becomes a baseline capability,” he concludes.