Seequent survey on AI for geoprofessionals: key data management lessons for engineers
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Geoprofessionals in mining and civil sectors are increasingly adopting AI tools but still struggle to extract value from complex, multisource subsurface datasets, according to Seequent’s 7th Geoprofessionals Data Management Report surveying over 1,000 practitioners. Respondents report data spread across multiple software platforms and large volumes of un-managed files, limiting effective integration of geological, geophysical and geotechnical information. The findings signal persistent bottlenecks in model building, QA/QC workflows and cross-discipline data sharing, despite wider availability of AI-assisted interpretation tools.
Technical Brief
- Scope is restricted to self-reported survey responses; no independent validation or project case back-analysis is included.
- Findings mainly inform software and workflow optimisation rather than prescribing specific design standards or regulatory practices.
Our Take
Seequent’s 7th Geoprofessionals Data Management Report sits within a cluster of AI/software coverage in our database where geoscience teams report spending over a quarter of their time on data handling, signalling that any AI tools Seequent embeds into its Bentley Subsurface stack will be judged primarily on how much billable technical time they can win back.
The Barro Alto nickel mine case in Brazil, which already uses Seequent’s Central and Leapfrog Geo for a live subsurface model, shows that operators with integrated platforms are best placed to exploit AI for real‑time decision support rather than just post‑processing, giving a template for how mining clients might operationalise the survey’s findings.
Within the 25 Software stories in our recent coverage, Seequent appears unusually often alongside other Bentley ecosystem tools such as OpenGround and Enviro Data, suggesting that AI features rolled out here could quickly propagate across a wider installed base in both mining and civil geotechnical practices.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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