Mineral exploration investment: 6 barriers and one AI-powered solution

Challenge 1: Demand for minerals is soaring

The world needs more minerals both to meet net zero targets by 2040, and to ensure security of supply for industrialized and developing nations alike. Not just an incremental shift but a step change – 40 times as much in some cases.

While the world’s mining industry is on track to meet demand for some of these materials, others are struggling. This year’s Global Critical Minerals Outlook by the International Energy Agency predicted that some would remain worryingly scarce without further action – and not just Rare Earths.

For example, based on announced mining projects in early 2025, the IEA believes copper will fall 30% short and is ‘particularly concerning due to declining ore grades, rising project costs, and a sharp slowdown in new resource discoveries.’

The high market concentration of minerals in certain countries also leads the IEA to conclude ‘there is a risk of significant shortfalls in supply if, for any reason, supply from the largest producing country is disrupted.’ This will result in higher prices for consumers and reductions in manufacturing competitiveness, warned the agency.

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Challenge 2: Discovering promising deposits is not getting any easier

Meanwhile, the effort involved in locating a profitable deposit seems to be heading uphill rather than down. It takes on average five years of work and $25m of investment to arrive at a workable deposit. But when all the potential sites are considered, the success rate for new finds is just 5%.

In short, if the planet is to achieve its sustainability and supply goals, the discovery of new mineral deposits needs to be accelerated, and the methods of gauging their long-term potential enhanced. We need a better hit rate.

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Challenge 3: Geologists are a shrinking breed

Today’s generation of experienced geoscientists is steadily retiring; there are fewer of them in the market to support mining organisations; the number of qualified geologists emerging from university is falling, indicating that this situation is unlikely to change. (The same can be said of mining engineers who also play their part further down the value chain in moving projects from exploration to development). Even turning to consultants is becoming harder. Consolidation in the consulting industry makes it tougher to find fully independent geoscientists whose firms have not already been involved in a project in some way. In short, there is…well… a shortage; not enough geoscientists to analyse the data, and too scarce a skill set to waste on tedious number crunching and data scrubbing.

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Challenge 4: Investors need a faster and simpler way to decide if a find is worth supporting

Crucial to the successful discovery of new deposits is a better way of evaluating each opportunity – a technique that delivers more rigour, greater speed, and increased accuracy and consistency. In particular, we need a swifter way for investors to judge which proposals they should support and where the best opportunities lie, so that high-potential sites get the go ahead sooner.  

A shortage of candidates is not the problem: investors can receive anything from a few dozen to 500 proposals a year. However, the effort involved in assessing them is colossal. Proposals are often 300+ pages long, and data rooms can contain multiple reports, in a myriad of folders, with diverse file types. The data within them may be unstructured, poorly organised (if organised at all), and difficult to compare like for like. Quality can vary enormously, and may drift from cold facts into marketing hype. 

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Challenge 5: Investment analysts can’t be expected to read data like an expert geologist

Meanwhile many investors may be experts in finance, and knowledgeable about the mining sector, but expecting them to wield the analytical prowess of accomplished geoscientists is unrealistic. 

Consequently their teams will spend too long trying to make sense of a proposition’s report, when they should be focused on judging its chances of success, or risk of failure. They need support to face the exponentially expanding amount of data they are expected to absorb, and they need a way of doing it much faster and more consistently. 

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Challenge 6: AI can cut through the data mountain… but not all AI is the same…

By its nature, AI is perfectly placed to shorten this process, and deflate vast amounts of often conflicting information into a succinct summary of vital facts. However, while many of us are starting to take this technology for granted on our phones, a simple web search is (clearly) very different from a complex mining operation, and there are issues to contend with. Off-the-shelf Large Language Models are built to be all-purpose tools. Jacks of all trades, not masters of one. There are lots of AI models in the market but only one precisely trained and dedicated to supporting mineral analysts in their role of getting to the next stage of investment appraisal – and at the maximum possible speed. Ours. 

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MinersAI is the only AI uniquely tuned to solve mineral exploration challenges

Our product is powered by specialist LLMs, trained by us on mining data, and explicitly purposed to analyse mineral exploration technicalities. 

• It can review and summarise proposals in hours, not days or weeks. 

• It can re-structure even disparate mining and exploration data into cogent arguments to support your investment thesis. 

• It’s backed by our in-house geological and data scientists who can bring a level of expertise that may not be available within your own business. 

• It can help free your experts’ time to concentrate on the future stages of investment appraisal, and therefore get projects to the investment stage faster, and with more accurate and rigorous screening. 

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What we’re building

A way for geologists to get their time back, and focus on what counts      

Predictive approaches to exploration, based on robust scientific analysis, hold the potential to dramatically improve the success rates for discovery of economic mineral resources. 

However, the data available to make these predictions, whether gathered by exploration teams or bought from commercial sources, is rarely in a condition where analysis is quick or straightforward. It may diverge in format, lack standardisation, and vary in quality or consistency. In short, it’s unlikely to ‘play nice’ together. 

While AI-powered mineral-system prediction can make an enormous contribution to the search for profitable deposits, it needs data that is aggregated, standardized and digitized to do so. And vast quantities of it. 

Present and future mineral explorers will come to rely on these huge data and AI models to pinpoint where to dig, and increase the success rate of discoveries. 

And that’s what MinersAI is building – the largest database of geological data ever put together; a foundational layer of geoscientific insight that will power, enable and accelerate mineral exploration for years to come. 

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Would you like to see how MinersAI platform can support your mineral exploration projects? Connect with us and book a walkthrough with MinersAI here.

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