New applications of artificial intelligence can be particularly beneficial for the mining sector, given its dynamic processes, substantial operational data, and geological uncertainties. However, the lack of AI integration across the industry may be hindering companies’ progress.
A new report has found some of Australia’s – and the world’s – largest mining and energy companies were not using artificial intelligence within their finance divisions, with its use significantly lower than in large businesses overall.
Financial modelling can encompass fluctuations in commodity prices or geopolitical changes, incorporating core variables such as price along with others, including diesel rates, development capital costs, royalty rates, and more, all embedded within the AI application.
Conducted by Perth-based Access Analytic in July and August 2025, the research collected data from over 60 companies, primarily mining firms in Western Australia, several of which reported revenues surpassing $1 billion.
The key finding was that use of AI in the mining and energy sectors’ finance divisions was substantially lower than in large WA businesses overall, with 28 per cent of respondents saying they had no plans to implement AI.
Of these, mining companies in WA were one of the largest groups at 67 per cent of respondents.
The research also uncovered several other important insights into how financial leaders of some of Australia’s largest companies were utilising AI.
Jeff Robson, Managing Director of Access Analytic, stated that the absence of AI-driven innovation in large mining and energy companies is undoubtedly hindering growth.
He said: “If you take mining, there are huge opportunities for those who are nimble right now.”
The report highlighted that chief financial officers often felt overwhelmed by the technology and struggled to determine where to start.
As a general guide, it detailed the top 10 practical actions financial officers can take to accelerate digital transformation in their organisations.
These steps included acquiring the right tools for the job, investing in data infrastructure, addressing integration gaps and enhancing capabilities, progressing beyond entry-level AI to enable strategic utilisation, and closing skills gaps while boosting confidence in AI usage.
The remaining recommendations included tailoring AI applications to the specific business context and data maturity, aligning investment drivers with organisational maturity, ensuring technology expenditure delivers strategic value, and matching future investments to capability needs and leadership ambitions.
Robson added: “With the flick of a button, mining innovators can evaluate the profitability of potential projects globally, or financial models can include shifts in commodity prices or geopolitical changes – AI can unlock the potential of companies, improving opportunities, productivity, and profitability.
“In 2025, boards should be asking their [C-suite] how they’re applying AI to better understand their data and improve productivity.
“Boards also need to consider both the threats and opportunities that AI brings.”
The report presented a potential application of AI in financial modelling through a hypothetical scenario where a miner is contemplating the acquisition of one of two lithium mines.
This consideration arises after an innovation involving lithium resulted in increased prices, rendering both mines economically viable.
Lithium has also just been placed on a critical minerals list, leading to several jurisdictions (including the host countries of the potential acquisitions) banning the sale of lithium to various target market countries.
One of these host countries is approaching an election, featuring two main political parties with differing policies on mining and export controls for lithium.
Using AI, mining companies can evaluate which incoming government would likely influence – and the extent to which – the mine’s profitability.
A paper published last year in the journal The Extractive Industries and Society detailed how recent advances in methods for optimisation and simulations highlighted AI capabilities
and AI-enabled applications as a promising tool for identifying a development venue leading to the desired outcome.
The authors noted that a rapidly growing body of research demonstrates how machine learning and AI-driven approaches could enhance mining economics.
They said: “Superior in data mining and analytics, AI algorithms used for projections and simulations help people choose the best patterns of behaviour and management practices, boosting productivity, optimising operations, and increasing profitability.
“Moving beyond economic and efficiency gains, AI-enabled tools have the potential to help improve the comprehensiveness or sustainability of decision-making in mining operations.
“By incorporating not only large amounts of economic data, but also significant amounts of data on environmental, land use, communities, and governance factors, multi-objective optimisation of operations through machine learning processes is potentially useful.”
They added that AI could contribute to more sustainable mining by better exploring the interconnectedness of various ecosystems and quickly identifying aspects like ground cover through AI-enabled image detection.
An important area the research explored was ethical considerations in the development and use of AI, as the technology did not necessarily create the ethical dilemmas often discussed, but had the potential to exacerbate or accelerate power dynamics and imbalances already at play.
The authors said: “For example, AI did not create the power imbalances between corporations and workers or communities.
“AI is not responsible for inequalities between high-income mining nations and low-income ones, but because the technology allows the player that controls it to speed up analysis, knowledge, and decision making, AI can amplify these trends.”
