By Bill Gough, Mining Industry Application SME, and Manuel Lopez, Mining Industry Account Executive, Imubit
The conversations in Vancouver confirmed that mining is ready to move from experimental pilots to enterprise-scale digital programs. Five themes stood out at the event, and each one matters for any operator trying to turn plant data into throughput.
Capital goes where the return is clear
Executives have stopped debating whether AI has potential and now ask whether a given project earns its keep. A panellist from RUA Gold was candid: a US$250k annual AI budget is "about the cost of two drill holes." If the technology raises the hit rate in exploration and drilling, the value quickly outweighs the investment risk.
For majors that move hundreds of millions of tonnes, fractional efficiency gains justify larger programs. For mid-tiers and juniors the bar is higher. A use case has to show up as ounces produced or dollars saved within the fiscal year. A technology that raises the hit rate of a drilling campaign can clear the hurdle rate for a gold producer of any size.
Cybersecurity is the gatekeeper
Technical debt and fragmented governance still stall promising pilots. Polling at the conference suggested that 40 to 60% of AI projects stop at the security team before they reach production.
The lesson is to involve cybersecurity leadership early, to quantify cyber-risk reduction alongside the operational upside, and to choose IT leadership that is open to change and ready to take calculated risks. Kalev Ruberg, recently retired after 17 years as CIO of Teck Resources, gave a useful perspective on balancing risk and innovation. Teck's multi-year Race21™ initiative set out to make it the most digitally innovative company in mining.
Digital twins mature when there is a roadmap
KPMG opened its session by challenging the audience's ideas about digital twins, with a reference to SimCity, and then laid out a practical framework for what a digital twin project should do.
- Give visibility into the data.
- Predict what comes next from what has already happened.
- Provide levers and, when they are pulled, show the impact on the business.
The case studies showed that the larger gain comes when a model can act on its insights as well as display them. ExxonMobil's SafetyPal, a retrieval-augmented language model that serves more than 3,000 field users, showed that getting to action is possible today when domain data, governance and change management line up.
Problem-focused pilots build credibility
The Mosaic Company stressed starting with the problem. The AI programs it has seen fail rolled out a large-scale solution first and then went looking for a problem to solve. That approach loses front-line stakeholders, because they do not feel heard early enough in the decision.
Mosaic has found that short, problem-focused pilots build credibility faster than multi-million-dollar initiatives. It then uses the momentum from those wins to go after bigger problems.
Talent translators are scarce
Technology alone will not close talent gaps in geology, metallurgy and data science. Speakers urged companies to develop "translator" roles: people who pair domain intuition with analytics fluency and can move between the mine, the control room and the boardroom.
Hiring for curiosity and systems thinking, alongside traditional mining experience and credentials, was a consistent theme. Without that human interface, even good technology struggles to earn trust on site.
Where closed-loop optimization fits
Imubit's experience with process optimization across the process industries lines up with the gaps raised in Vancouver.
- Start with the problem. Each project begins with a defined operating problem, an economic baseline and a target return, the same discipline Mosaic described.
- Secure, explainable models. Explainability built into an operator-friendly interface builds trust. The models run on-premise and write to the site's existing APC or DCS, which addresses many of the risk concerns IT leadership raised.
- A digital twin that acts. The approach follows the three steps in KPMG's framework. A Deep Learning Predictor learns how the process behaves from its own history, including what the plant measures and what it does not, and predicts what happens next. A Deep Learning Controller, trained by reinforcement learning, then moves the relevant setpoints toward the best economic outcome, inside limits set by plant engineers.
A practical starting point
Autonomous process optimization in mining is now within reach. Sites that line up the right internal and external support, and plan for organizational change, put themselves in a stronger position.
For an organization exploring where AI can add value, a sensible first step is a problem with direct operational impact and real operator interaction. Bringing operations along as the models are built is what earns buy-in, whether the target is higher throughput, lower energy intensity or tighter environmental, social and governance metrics.
In mining, minerals and metals, Imubit's published results are a 1 to 3% metal-recovery improvement, 5 to 10% grinding-energy savings and a 2 to 5% throughput increase.
