RESEARCH REPORT
From Explore to ore
How data and AI can radically tighten the mineral exploration lifecycle
10-minute read
April 21, 2025
It’s no secret that mineral exploration companies face considerable challenges.
Geopolitical risks are growing. Obtaining regulatory approval for exploration projects is getting harder in many places, as local communities, environmental groups and other stakeholders demand more sustainable practices.
In the meantime, discoveries of new mineral deposits are less frequent and exploration timelines have extended significantly.
It now takes about 40% longer, on average, than it did just 15 years ago to get from discovery to production, as shown in the figure below.
Figure 1: Time to first ore continues to increase
Source: S&P Capital IQ pro, Accenture analysis
As companies seek to “tighten,” or shorten their exploration lifecycles, artificial intelligence offers invaluable possibilities to help firms discover economic deposits faster and at a lower discovery cost. This report explains how certain companies are already turning AI’s potential into tremendous results.
Despite the benefits that some exploration companies are reaping from AI, many firms are still not making the most of this capability. This report identifies three priorities—invest more, become data-ready and refocus talent and skilling strategies—that would enable exploration companies to realize the full value of AI.
Our research shows that four activities typical of mineral exploration are especially suitable for AI-led reinvention:
1.
Prospectivity analysis and target generation
2.
Advanced mapping and surveying
3.
Analysis of drill data and ore body knowledge
4.
Permitting and compliance
Mineral exploration companies face numerous challenges
Geopolitical risks are mounting for the industry, with access to minerals increasingly being used by governments as chips in their countries’ trade wars.1 In response to this, many countries and regions are adjusting legislation to encourage, support and fast-track development of in-country critical mineral deposits.
Canadian legislation around restricting foreign investment and ownership was introduced in 2022, with updates in 2025 focusing on the size of the business, its place in the innovation ecosystem and, critically, the impact on Canadian supply chains—in an effort to “ensure Canadian interests remain adequately protected.”2 In March 2025, the European Union selected the first 47 projects that it believes will strengthen local extraction in the bloc, a key step in implementing the EU’s Critical Raw Materials Act 2024.3
Stakeholders’ expectations for environmental and social governance around mineral exploration activities are changing too. Many mineral exploration projects have been put on hold in recent years due to growing public scrutiny as communities, governments and environmental groups demand stricter environmental and social safeguards, including greater transparency and stronger commitment to sustainable practices, before granting project approvals.4
Meanwhile, in some areas, drilling costs are also going up. In Canada, on average, drilling costs went up from $140/meter in 2014 to $201/meter in 2023, as shown in Figure 2.
Figure 2: Up and up
Drilling costs in Canada have risen significantly since 2020
Source: Institut de la statistique du Québec
These days, it also takes a lot longer to get to production of ore and therefore generate revenue.
For the average mine that came online from 2005-2008, it took 12.7 years to go from discovery to production, according to research published by S&P Global.
Between 2020-2023, the comparative figure was 17.9 years—40% longer.5 About one in 1,000 exploration projects results in a viable mine.6
Faced with mounting geopolitical risk, growing stakeholder demands, rising costs and longer lead times, exploration companies have also had to make do with only modest increases to their budgets. S&P reports that in 2010, 2,213 public and private companies with exploration budgets in excess of $100,000 collectively spent $11.5 billion exploring for non-ferrous metals; in 2023, the comparative figure was $12.9 billion.7
While the total number of drillholes reported globally rose from 25,356 in 2014 to 53,582 in 2023 (the most recent year for which data is available), a decline of 23% was recorded between 2022 and 2023, most notably for gold and copper (Figure 3).8 This was likely influenced by geopolitical uncertainty driving up gold prices and the widely expected shortfall in copper supply by 2027.
Figure 3: Up and down
Drilling activity has been inconsistent over the last decade
Source: Institut de la statistique du Québec
Companies are increasingly investing in extending the life of existing mines. This is due in part to dwindling Mineral Reserves as well as declining grades; and because it is faster and cheaper to get minerals to market from existing operations where the infrastructure already exists. Going through the whole exploration lifecycle with a greenfield project—with all the permitting and social-license-to-operate hurdles that such a project entails—requires a much longer timeframe.
The impact of this can be seen in the share of mining companies’ exploration budgets devoted to “grassroots” exploration. From 2010 to 2023, these budgets declined from 33% to 23%, while the share of budgets allocated to “minesites” rose from 24% to 38% (Figure 4).11
Up to
12.7
years to get from the discovery stage to the production stage.9
Only one in
1,000
exploration projects result in a viable mine.10
Figure 4: Less grassroots
Minesite exploration is receiving a greater share of exploration budgets
Source: S&P Global
Meanwhile, the number of active exploration companies (public and private) with exploration budgets in excess of $100,000 has plateaued globally. In 2010, there were 2,213 such companies; in 2023, there were 2,238.12 Most near-surface, large deposits have already been found, which is also a factor in the shift in focus to brownfield exploration and the number of exploration companies remaining flat.
Evidence that discoveries are harder to come by can be seen in the drop in major copper and gold discoveries over the last 15 years. Between 2010 and 2016 (Figure 5) there were 22 significant copper discoveries and 36 significant gold discoveries. Between 2017 and 2023, these numbers had dropped to seven for copper and 11 for gold.13 The move from near-surface exploration to under-cover exploration (where deposits are covered by layers of rock) requires a change in perspective, technique and the leveraging of new technology.
Figure 5: Bonanza no more
New discoveries of gold and copper are becoming less common
Brownfield exploration: challenges and opportunities
“Brownfield” exploration occurs near existing mines. It involves looking for satellite deposits, extensions to existing deposits or reexploring former operations that may have been shuttered for decades. Often, brownfield exploration can lead to the discovery of high-quality satellite deposits that can feed the existing mine and processing infrastructure. This, in turn, can reduce the need for new capital investment and keep existing operations going.
On the other hand, lower-quality deposits, which can also be found through brownfield exploration, require more extensive processing—and, thus, increase costs. Aging mines also need more maintenance and investment to remain productive. Aging mines are typically more energy and water intensive as well, which can lead to both higher carbon emissions and increased regulatory scrutiny.
How AI is transforming the exploration lifecycle
As mining companies face these challenges, improvements in data and artificial intelligence, including generative AI, are allowing firms to reinvent how they search for minerals and gather ore-body knowledge. Here’s how.
Collation and curation of geological, geophysical and geochemical data traditionally requires significant manual effort, geoscience expertise and time-consuming processes. AI can help companies rapidly uncover correlations within vast, complex datasets—thereby increasing speed to discovery through faster analysis and geological modeling, accelerating decision-making, reducing discovery costs and mitigating risk.
In other words, AI is bringing incredible speed, precision and scalability to data collation and analysis. In our experience, four key activities of the exploration lifecycle are being transformed by AI.
01
AI accelerates prospectivity analysis and target generation
One of the biggest challenges in exploration is deciding where to explore for mineral deposits, though prospectivity analysis for area selection and reconnaissance field work help narrow down the area. In the past, geoscientists needed to painstakingly analyze both remote sensing data and historical data, collect samples from the field and then make decisions about the prospectivity of an area. This process, however, is often slowed by the fact that an exploration company’s data is often not available in a single portal. As a result, the time required to collate and process the data into an accessible and analyzable format often leads to unnecessary effort and delays to decision making, as well as higher costs.
With the support of AI, prospectivity analysis can be undertaken more rapidly to greater success. Machine-learning algorithms can analyze multi-discipline geoscience data to identify geological trends, compare with known deposit styles and then predict high-prospectivity areas with much greater accuracy and at a faster rate.
Consider a few examples.
- Fleet Space Technologies is enabling mineral-exploration groups to leverage satellites for multi-physics modeling and updates of those models in significantly reduced timeframes. The company uses AI to process multispectral and geophysical data to identify subsurface anomalies. The rolling updates of the models are opening new frontiers for mineral discovery, particularly with deposits under cover—such as Australia’s Macquarie Arc—and accelerating discoveries.14
- VerAI Discoveries is using AI in its “mineral asset generator”, focusing on geophysical data to identify prospective ground, particularly deposits under cover. Once identified remotely, the company then seeks to acquire the exploration license and/or partner with local entities to accelerate exploration on the ground. VerAI Discoveries is applying this approach in Chile, utilizing AI as a tool for portfolio review to make more informed decisions—both where to acquire and when to drop “staked ground”. By doing this, the company reduces risks by ensuring that funds are applied only to the most promising areas.15
02
AI facilitates advanced mapping and surveying
Geological mapping is central to exploration, but traditional mapping techniques rely on manual interpretation, which can be subjective and prone to errors. Many exploration areas are remote, hard to access, covered in dense vegetation or terrain hazards and require multiple land access permissions—so mapping and surveying are slow processes.
Digital terrain models are essential for accurate mapping, surface sampling location and placement of drill collars, ensuring that the data captured, samples taken and any ensuing assay results have known coordinates and therefore can be used for modeling.
Today, AI-powered remote sensing tools are increasingly being used across the industry. Using satellite and drone-based imaging, a digital elevation model (DEM) or digital terrain model (DTM) can be rapidly generated and alteration and mineral signatures detected remotely. This reduces footprint, cost and risk for field workers, and enables the explorer to generate targets remotely, reducing early-stage exploration activities that may not be fruitful.
- For instance, BHP now uses drones that are equipped with military-grade cameras. These drones are being used for “mineral surveillance”, among other use cases, and provide real-time aerial footage and 3D mapping. “This is far cheaper than using planes for survey work,” said Frans Knox, BHP’s head of production for mining, to MINE Australia, a magazine. BHP estimates that replacing plane-based surveys with advanced drones has saved the company AUD5 million annually at its sites in Queensland, Australia alone.16
- Esri’s geographic information system software, “ArcGIS”, is widely used across exploration and enables geoscientists to synthesize field data, using mapping and analysis tools to visualize, share and communicate field observations. To support the use of AI, Esri has made 75 pre-trained AI models available in ArcGIS for various uses, including the identification of buildings, roads and vegetation, which can assist with field-work planning and logistics. In addition, Esri has integrated geospatial AI capabilities into ArcGIS to support AI workflows, including the building and training of AI models.17
03
AI enables improved analysis of drill data and ore body knowledge
Traditional drill-data analysis relies on manual core logging and laboratory-based assays— the results of which take weeks, if not months, to return. This limits real-time decision-making during the drill campaign, increases exploration costs and increases the risk that critical geological insights are missed.
AI-powered core-scanning technologies—including hyperspectral imaging and high-resolution core photography—can provide geoscientists with rapid, accurate and consistent drill-core insights without waiting for traditional assay results. Couple this drill data analysis with AI-enabled, 3D predictive models, and geoscientists are equipped to make in-field, informed decisions, such as calling the end of a hole or determining the next drilling location. This results in significantly shortened exploration timelines and considerably improved operational efficiency.
- Hyperspectral imagery and laser-induced breakdown spectroscopy (LIBS) are becoming increasingly common. LIBS provides geologists with insights on lithology, alteration and mineralization in the field to support core logging—and LIBS does this far more quickly than the return of assay results. For example, Corescan’s Hyperspectral Core Imager incorporates AI sensors into auto-scan core trays, rock chips and other sample material, providing high-speed data acquisition, quality control and system-health monitoring, while doing all this on-site in remote environments.18
- St Barbara, an Australia-based mining company, leveraged advanced AI algorithms to classify material types in their operations based on recovery characteristics, identifying additional sulfide materials suitable for carbon-in-leach treatment. This allowed the firm to reclassify 3.7 million tons of material at 1.2 grams per ton of gold at its Simberi mine in Papua New Guinea. As a result, St Barbara can now recover more gold from existing resources through its existing flowsheet, increasing revenue and reducing waste—without additional exploration expenses.19
04
AI streamlines permitting and compliance
Regulatory approvals remain one of the biggest obstacles to advancing exploration projects, from discovery to first ore. Obtaining permits requires extensive documentation, environmental assessments and adherence to evolving regulations. These hurdles often delay projects by months or years.
AI can streamline the permitting process by analyzing regulatory requirements, as well as historical permit approvals and project-specific factors, such as environmental and social impact assessments, heritage data, biodiversity considerations and proximity to protected areas as well as communities. AI can generate draft applications, ensure compliance with environmental standards and predict potential regulatory hurdles so they can be addressed before they arise. Among other benefits, AI’s ability to streamline permitting and compliance reduces the scope for human error, speeds up submissions, supports standardization of submitted materials and enables companies to navigate complex legal frameworks far more efficiently.
- A leading iron ore producer in Western Australia streamlined permit approvals by developing a sophisticated digital platform, which manages over 1,000 requests annually.20 The system automates compliance checks, integrates geographic information system mapping, helps the company comply more efficiently with the many environmental and heritage site regulations, and supports its maintenance of the social license to operate.
Unlock AI’s full potential
In our full report, we describe three actions that mineral exploration companies can take to activate the power of AI:
- Increase investment
- Become data-ready
- Refocus talent and skilling strategies
AI is a breakthrough technology. Exploration companies that figure out how to use it effectively before their rivals do will receive their own huge rewards. Embrace the transformative power of technology and set out on a journey toward a more prosperous and resilient future.
Contact us today to learn more about how your company can achieve its goals through data and AI.
References
1 CSIS: The Geopolitics of Critical Minerals Supply Chains
2 Government of Canada: Updated Guidelines on the National Security Review of Investments
3 Mining.com: EU selects 47 strategic projects to secure critical minerals access
4 Financial review: Gold miner says $1b project ‘unviable’ after Plibersek intervention
5 S&P Global: Average lead time almost 18 years for mines started in 2020–23
6 Northern Gold Insights: Economic Factors to Consider in Mineral Exploration
7 “Non-ferrous” metals are those that are not primarily composed of iron, such as copper, aluminum, nickel, zinc, lithium, cobalt, gold and silver. See S&P Global: Average lead time almost 18 years for mines started in 2020–23
8 S&P Global: World Exploration Trends 2024
9 S&P: Global: World Exploration Trends 2024
10 Northern Gold Insights: Economic Factors to Consider in Mineral Exploration
11 S&P: Global: World Exploration Trends 2024
12 Ibid
13 S&P Global: Gold from major discoveries grows 3%, although recent discoveries remain scarce
15 GBR: Chile Mining report 2024
16 Mine Digital: How drones are changing the art of mineral surveying
18 Corescan: Hyperspectral Core Imager