Ask most leadership teams in mining or oil and gas whether AI is on their agenda and the answer is yes. Ask whether the organisation is ready to scale it past a pilot, and the honest answer, for most, is no. The data backs that up more clearly than most people in the industry would expect.
Mining is behind, and it knows it
PwC’s Mine 2026: Ambition to Action report found mining had the lowest score of any sector reviewed on its AI fitness index, attributing the gap to insufficient innovation investment and weak data governance foundations (PwC, June 2026). It is not a case of leadership being unaware either: 40 per cent of mining CEOs surveyed said their organisation’s technology performance had fallen below their own expectations. The same report found the companies that are genuinely AI-fit achieved 7.2 times higher performance gains, through a combination of revenue growth and cost reduction, than their peers. The gap between the leaders and everyone else is not marginal.
Oil and gas has the tools, not yet the transformation
The picture in oil and gas is different but tells a similar story. EY’s US AI Pulse Survey (Wave 4, fielded December 2025, published April 2026) found energy sector leaders reporting a 72 per cent increase in organisational interest in responsible AI over the past year, the largest increase of any industry surveyed. But EY’s analysis of the oil and gas subsector specifically found organisations already possess advanced AI models, digital twins and analytics capabilities, while legacy infrastructure and organisational silos are what limits transformation at the enterprise level (EY, April 2026). The barrier sits in the organisation around the technology, more often than in the technology itself.
That same research found 56 per cent of energy leaders struggle to directly link productivity improvements to their AI efforts, well above the 35 per cent average across all industries surveyed, and 72 per cent believe more training is needed just to communicate the productivity gains that are happening. Readiness, on this evidence, is as much a measurement and communication problem as it is a technology one.
The talent gap is compounding it
Workforce readiness adds another layer. PwC’s Mine 2026 report, citing the World Economic Forum’s Future of Jobs Report 2025, found 39 per cent of mining employers anticipate that difficulty attracting the right talent will impede their transformation initiatives. An organisation can have the right platforms and still stall if it cannot resource the people who need to run them.
What “ready” looks like
A proper AI readiness assessment does not just ask whether you have used a large language model somewhere in the business. It looks across seven areas: strategy and leadership, use case adoption, data and infrastructure readiness, AI governance and ethics, people and skills, tools and platforms, and change management and culture. Most organisations are strong in one or two of these and materially behind in the rest, and that imbalance is exactly what stalls AI initiatives after the pilot stage.
Find out where you stand
If your organisation is somewhere between “we’ve tried something with AI” and “we’re scaling it with confidence,” an AI readiness and maturity assessment gives you an objective, evidence-based baseline across all seven areas, with a practical roadmap to close the gaps that matter most. Book an assessment to find out where your organisation sits.