Waste rock is one of those costs that mine operators have always treated as inevitable. You drill, you blast, and you accept that a certain percentage of the material you move is going to be non-economic rock that just needs to go somewhere. The question has always been how much waste is genuinely unavoidable versus how much is a product of imprecise drilling.

Turns out, quite a lot of it falls into the second category. And AI-driven drill pattern optimization is starting to prove that in a meaningful way.

The Problem with Traditional Drill Patterns

Conventional blast design typically relies on standardized patterns — fixed spacing, fixed burden, and adjustments based on the experience and judgement of the drill and blast engineer. It’s not a bad approach. Good engineers know their geology and can adjust patterns based on what they’re seeing in the rock. But they’re working with limited data and making decisions that affect thousands of tonnes of material movement.

The issue is variability. Rock mass properties change across a blast block — hardness, fracture density, moisture content, structural orientation. A pattern that works perfectly in one section might create excessive overbreak or poor fragmentation in another. When that happens, you’re moving more waste rock than necessary, and your processing plant is dealing with inconsistent feed.

Multiply that across hundreds of blasts per year, and even small improvements in pattern accuracy add up to serious money.

How AI Changes the Equation

AI-powered drill pattern optimization takes a fundamentally different approach. Instead of applying a standard pattern with manual adjustments, the system ingests data from multiple sources — drill monitoring systems (measure-while-drilling), geological models, previous blast performance data, fragmentation analysis from cameras, and even weather conditions — to generate optimised patterns for each specific blast.

The algorithms identify zones within a blast block where rock properties change and adjust hole spacing, burden, depth, and explosive charge accordingly. In hard, competent rock, the system might increase spacing to reduce drilling costs. In fractured or weak zones, it tightens the pattern to prevent excessive overbreak.

What makes this different from earlier computerised blast design tools is the feedback loop. Every blast generates data — fragmentation results, muckpile profiles, wall conditions, vibration measurements — and the AI model learns from each one. The system gets better over time in ways that a static design tool simply can’t.

What the Numbers Look Like

The results coming out of early adopters are compelling. Several large Australian open-pit operations have reported waste rock reductions of 8-15% in areas where AI-optimised drill patterns have been deployed. That doesn’t sound massive until you consider the volumes involved.

On a mid-sized iron ore operation moving 80 million tonnes per year, a 10% reduction in waste rock movement translates to 8 million fewer tonnes to haul, dump, and manage. At typical haulage costs, that’s tens of millions of dollars annually.

Beyond the direct cost savings, there are downstream benefits. Better fragmentation means less secondary breakage, which means less wear on loaders and crushers. More consistent feed to the processing plant improves throughput and recovery. And tighter blast control means better final wall conditions, which has obvious safety and geotechnical stability implications.

A consultancy we rate has been involved in helping mining operations evaluate and integrate these kinds of AI-driven optimisation systems, and they’ve noted that the biggest challenge isn’t the technology itself — it’s getting the data pipelines right. You need reliable, real-time data from your drills, and many older rigs don’t have the sensor packages to support it.

The Implementation Reality

This isn’t a plug-and-play solution. Successful deployment requires several things to come together:

Data infrastructure. Your drill fleet needs to be equipped with MWD (measure-while-drilling) sensors that capture penetration rate, torque, vibration, and rotation pressure at high resolution. If you’re running older rigs without this capability, you’re looking at retrofits or replacements.

Geological model integration. The AI system needs to talk to your geological and resource models. That means your geology team needs to keep models current and ensure data formats are compatible.

Blast performance measurement. You need consistent post-blast data — fragmentation analysis from camera systems, drone surveys of muckpile geometry, and wall condition assessments. This is where many operations fall short. They do blast design well but don’t systematically measure outcomes.

Operational buy-in. The drill and blast engineers need to trust the system’s recommendations. That takes time and transparent results. The most successful implementations I’ve seen involve the AI system presenting recommendations alongside reasoning, so engineers can understand why a particular pattern was chosen.

What Comes Next

The direction of travel here is clear. As more operations deploy these systems and share performance data (anonymised through vendor platforms), the models will continue to improve. We’re also seeing integration with autonomous drilling systems, where the AI doesn’t just design the pattern — it directs the drill to execute it with centimetre-level precision.

The mines that adopt this technology early won’t just save money on waste rock movement. They’ll build a data advantage that compounds over time, as their models learn from more blasts in their specific geological conditions.

For operations still running standardised drill patterns and adjusting on gut feel, the question isn’t whether AI-optimised blasting makes sense. It’s how quickly you can get the data infrastructure in place to support it.