Key Takeaways

  • Block modelling itself still runs on established commercial platforms: Datamine Studio RM, Maptek Vulcan, Hexagon HxGN MinePlan, and Deswik.
  • AI is arriving as an add-on layer, not a replacement — Datamine’s 2026 MineScape release added AI-enabled scheduling assistance built on top of existing block models.
  • This is marked “Emerging” deliberately: the core block-modelling engines are mature and largely non-AI; the AI value is concentrated in newer scheduling/optimization modules bolted onto them.
  • If you’re choosing a platform, the question isn’t “which one has AI” (most are adding it) but “how mature is the specific AI module you need.”

TL;DR

Build your mining block model in an established platform (Datamine Studio RM, Maptek Vulcan, Hexagon HxGN MinePlan, or Deswik), then look at the AI-enabled scheduling/optimization modules these vendors are now layering on top for the actual intelligence gains.

How Do I Optimize Mining Block Models With AI?

A mining block model — the 3D array of blocks carrying grade, geotechnical, and geomet attributes used to plan extraction — is not itself an AI artifact. It’s built the same way it’s been built for decades: estimation (kriging, indicator methods, or increasingly ML regression) populates each block from your resource model, geotechnical model, and geomet model. What’s changed recently is what happens after the block model exists.

Datamine’s 2026 MineScape release is a useful example: it kept the underlying block-modelling engine largely as-is but added AI-enabled scheduling assistance that consumes the block model and helps generate mine schedules faster than manual iteration. Similarly, Maptek Vulcan, Hexagon HxGN MinePlan, and Deswik all compete on the same core capability (turning raw drilling/sampling data into a usable 3D block model) while differentiating increasingly on the optimization and scheduling layers built around it — see our companion posts on Long Term Planning and Mining Predictions Report for where that AI value actually shows up.

Practically: if you’re choosing a platform for the block model itself, the four options above are all mature and largely equivalent in core capability — pick based on your existing toolchain, licensing, and team familiarity. If you’re specifically chasing AI value, look one layer up at the scheduling, optimization, and reconciliation modules each vendor offers on top of the block model, since that’s where the genuine machine-learning and AI-assisted work is currently concentrated.

Try It With Geocluster

Figuring out which vendor’s AI-scheduling layer is actually mature enough to trust — versus marketing copy — takes real comparative research. The Geocluster Research Harness is built to help teams do that kind of systematic AI-tooling evaluation for mine planning workflows.