The Kitwe disaster: A call for AI and clean data in mining safety

A new Bloomberg report on the Kitwe dam disaster in Zambia suggests that its impact is 30 times worse than first estimated, meaning that the equivalent of 400 Olympic-sized swimming pools of toxic waste was released into the environment. Alastair Bovim, CEO of Insight Terra, believes that artificial intelligence (AI) is the answer to timely detection.
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Image credit: AP

“Things fail slowly until they fail fast,” says Bovim.

“That’s why timely detection and action matter - not only to prevent environmental harm, but to avoid the huge social, environmental, and financial costs of reacting after a collapse of this scale.”

Bovim argues that AI is not a silver bullet. Rather, it’s a dynamic force multiplier for engineers and frontline teams.

By taking on the dangerous, dull, dirty, and difficult tasks that humans shouldn’t or can’t do continuously, properly configured AI helps people act earlier and more decisively.

The 4Ds of AI

Critical sectors, from mining to oil and gas and agriculture, operate in some of the toughest risk environments.

They often function in remote locations, face climate risks, and are under mounting pressure from regulators and the public to demonstrate environmental responsibility.

In practice, the “4Ds” translate into concrete, real-world interventions, showing how AI and trusted data can shift monitoring from reactive checks to proactive prevention:

  • Dangerous: Tasks that put human life at risk - for example, manual inspections on unstable tailings dam crests or sending teams into flooded or contaminated areas during a disaster.

  • Dull: Automating repetitive, error-prone tasks such as manual reading transcription and daily logs. Automatically analysing subtle data shifts and flagging early warnings.

  • Dirty: Monitoring hazardous materials remotely - acidic water, toxic slurries - without exposing teams.

  • Difficult: Turning vast, fragmented data from satellites, sensors, and historical records into clear insights that reveal early warning signs of risk.

While mining provides a clear example, the principle applies beyond extractive industries – to wherever continuous, trustworthy data can prevent harm and inform better decisions.

Dynamic element

Using the 4Ds approach, AI adds a dynamic element to environmental risk management by turning intermittent human-dependent checks into continuous, contextual insight.

For example, if heavy rain is forecast, an integrated system can combine short-range weather models with ground sensors and satellite imagery to flag rising tailings dam water levels days in advance, triggering pre-emptive pumping or diversion – avoiding a flood before it happens.

Pressure or deformation changes in a dam, monitored alongside rainfall spikes, can be triaged automatically and routed to the right engineer with an evidence-based action plan as required by the Global Industry Standard on Tailings Management (GISTM).

Remote AI-enabled cameras, water-quality sondes, and automated analytics remove the need for people to enter hazardous sites.

AI analytics consolidates and converts thousands of raw readings into controls and real-time lead indicators, helping prioritise interventions quickly - saving time, reducing risk exposure, and avoiding expensive emergency responses.

Data is essential

The power of AI is only as strong as the data behind it.

As Bovim notes, the industry adage still holds: garbage in, garbage out. High-quality, contextualised data is essential for reliable insights.

"Poor inputs create misleading outputs," he cautions. Without rigorous, real-time data quality controls, GenAI solutions risk doing little more than automating - and amplifying - bad decisions at speed.

That’s why the focus must be on creating an immutable, auditable single source of truth, seamlessly ingesting data from multiple instruments, validating it within an engineering context, and flagging anomalies for expert review.

Transparency is critical. Engineers need to see the calculations, reproduce the results, and trust the full chain of custody behind every alert.

Misalignment

Global frameworks such as the GISTM are shifting expectations from episodic compliance to integrated performance monitoring.

Yet implementation is uneven: local standards in many jurisdictions lag GISTM, and there is an observable tendency for companies to meet the lowest legal bar rather than the more rigorous global standard.

That misalignment leaves communities exposed and undermines public trust - and it makes continuous, auditable monitoring both a practical and reputational imperative.

Bovim stresses that technology must empower people, not replace them.

“AI should free engineers to apply their extensive training and judgement where it matters most,” he says.

“It should amplify community-based monitoring, enhance emergency response plans, and make transparency the default.”

Crucially, early detection is also good business: timely interventions avoid the much higher costs of reactive clean-ups, litigation, and reputational damage.

Measurable steps

The Kitwe disaster is a reminder that prevention is not only possible but essential.

Investing in continuous monitoring, trusted data pipelines, and engineer-centric AI can turn early warning into timely action – saving lives, livelihoods, and landscapes.

Industry leaders and legislators alike must move beyond abstract debate and make real-time tailings monitoring a regulatory requirement, backed by enforceable standards.

Measurable steps include robust water management, well-defined trigger actions with tested response plans, and continuous, auditable monitoring that communities can trust.

Data that is late or wrong is no better than silence - it’s a lost opportunity to prevent harm,” Bovim warns.

“But when data is timely, accurate, and transparent, it empowers people to act decisively.

"That’s more than efficiency - it’s the difference between risk and resilience, between disaster and protection of lives and the environment we all share."


 
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