The Geotechnical Engineer in 2030: Why AI Will Transform (Not Replace) Our Role

Will the geotechnical engineer's role look the same in five years as it has for the last decade? As AI capabilities accelerate at breakneck speed, we are ncreasingly convinced the answer is no.

With rumours that GPT-5 may launch in the coming days and AI capabilities continuing to accelerate, maybe now is the time to consider how this might look. Geotechnical engineers on site today face a daily struggle with sensor data, inspections, models, and reports where critical data can be missed simply because there's too much to process.

Why Now?

Just a few years ago, the AI tools we have today would seem something out of science fiction. But the pace of advancement has been extraordinary. LLMs have shown that AI can interpret complex instructions and chain together multiple tools to accomplish goals. The next generation promises even stronger reasoning capabilities.

Additionally, new frameworks are making it easier to integrate AI with existing software. The Model Context Protocol (MCP) allows LLMs to interface directly with complex tools such as 3D modelling software (see RhinoMCP). These tools are still at their early stages but are we starting to get to a point where the limiting factor is no longer the technology itself, but how creatively and responsibly we apply it to our domain?

Envisioning an AI Orchestration Layer

Now imagine connecting these specialised capabilities under one intelligent orchestrator. Instead of juggling multiple applications or data sources, you'd interact with a single AI that uses all of them as needed. Here's how it might work in an open pit setting:

1. Hazard Detected: The AI continuously monitors real-time sensor data and spots an unusual deformation pattern in the north wall

2. As-built Performance: It automatically checks if the affected area deviated from design specifications. Are catch benches narrower than planned? The AI compares survey data against the original design parameters.

3. Context Analysis: The system pulls recent geotechnical mapping, inspection reports, and historical stability analyses for that sector. It identifies that recent mapping showed weaker geological units not captured in the original model, and a recent inspection noted new tension cracks forming along the crest.

4. Stability Model Update: Using the pit geometry, updated rock properties from recent mapping, and current pore pressure data, the AI runs a revised stability analysis.

5. Output: You receive a report highlighting the convergence of factors (steeper geometry, weaker materials, rising pore pressures), and the model factor of safety.

Throughout this entire process, the system keeps you informed at each stage, explaining what it's analysing and why, ensuring you can validate its reasoning and steer the system as needed. This maintains engineering oversight while dramatically accelerating the analysis workflow.

Benefits and Implications

If this kind of geotechnical AI assistant can be developed, it could dramatically improve efficiency. Engineers could focus on interpretation and decision-making rather than drowning in data processing and model preparation. There's also potential to enhance safety. An AI that never tires of monitoring data could catch subtle warning signs that might be overlooked on a busy day.

Crucially, none of this replaces human expertise. If anything, it makes engineering judgment more important. We'll always need seasoned engineers to guide the AI, interpret its findings, and make the hard decisions. The end goal is simply to let AI handle the drudgery so we can focus on solving real-world problems.

Are the Building Blocks Already Emerging?

This might sound ambitious, but the foundation for this future is already being laid. At Geotech Assist, we've been developing exactly these types of building blocks. Not general-purpose chatbots, but "expert assistants" tailored to specific geotechnical tasks. Our guiding principle remains constant: AI with engineering oversight. These tools augment the engineer's work, but final interpretations and decisions always remain with the human expert.

Conclusion

So, will the geotechnical engineer's role look the same in five years? We don't think so, but it won't be diminished. If anything, it will be elevated. We'll spend less time wrestling with data and more time applying our expertise where it matters most.

There are significant challenges around data quality, model validation, and ensuring proper oversight. However, the trajectory is clear to us.