Mining leaders today are being asked to do more with less: extract value, reduce costs, and raise safety standards all at once. On paper, technology should make that easier. But in practice, the sector has learned to treat bold digital promises with care. Its skepticism isn’t stubbornness, but the product of experience.

Unlike some industries that welcome experimentation, the Mining and Minerals sector recognizes that novelty alone isn’t enough to drive progress. Where risks are high, decision cycles are long, safety is uncompromising, and capital decisions are conservative by necessity, the big promises and ambitious visions that so many technology providers offer rarely match reality. Wrong decisions carry consequences that go far beyond financial performance – which is why experimentation for its own sake is a risk the sector can’t afford to take.

Mining environments demand a more holistic kind of intelligence that includes AI, but is grounded in industry context, real outcomes, and tangible proof instead of vision.

The Technology Trust Gap

All of this stems from the fact that the mining sector has been burned in the past. Previous experience of tech solutions that promised much, but under-delivered, have left trust fragile and made credibility even harder to earn. It’s a reality I’ve seen firsthand, with many operators having lived through transformation initiatives that sounded compelling in theory, but failed to improve the things that actually matter.

This is where the Mining and Minerals sector is unique in its adoption of technology. Leaders can quickly sniff out unsubstantiated claims or overzealous promises – especially when they don’t hold up in real-world conditions.

In reality, the priorities have always been the same: keep people safe, maximize production, and deliver asset performance at the lowest sustainable cost. Digital capability is now table stakes, but what matters is relevance.

Where intelligent technology has fallen short before has not been from inadequate tools, but because they’re too often applied to the wrong problems. Solutions are introduced as a ‘silver bullet’ without being anchored in the realities of the job: tons moved, recovery rates, downtime, safety exposure, and operating cost. Technology becomes noise instead of progress. In environments like these, it is context and lived experience that should lead thinking.

Reframing Intelligence for Tangible Outcomes in Mining & Minerals

Mining demands a different kind of intelligence: one that’s focused and grounded in judgment. AI and technology can be powerful enablers when applied with consideration and discipline, but they are not the sole key to unlocking future potential in mining, as some technology-first vendors make them out to be.

This is why intelligence should always start with the problem instead of the solution. When we augment (or complement) human expertise, operational understanding, and domain experience with digital capability, we are able to embed intelligence in a way that translates into meaningful mining outcomes – whether that’s tons, recovery, safety or cost. The impact becomes tangible instead of visionary.

At Cyient, we have a name for this: Embracing Intelligence. Practically speaking, it means being problem-first, outcome-led, and anchored in industry reality. So that when we apply tech and AI, we do so in a way that builds confidence, safety, and trust without adding risk.

What Embracing Intelligence Looks Like in Practice

When intelligence is applied this way, it changes how work gets done.

Often it means looking beyond the ‘surface level’ problem and identifying the underlying constraint that is actually limiting performance. That requires challenging assumptions, reframing priorities, and sometimes slowing things down to get the fundamentals right.

In practice, this means:

1. Starting with the problem, not the solution

I regularly encounter situations where a mining organization comes to us with a preferred solution already selected, before we’ve had a chance to really look at where things might be going wrong. But intelligence starts by pausing that conversation and asking the important questions:

• What operational outcomes need to improve?

• What behavior needs to change?

• How will success be measured in mining terms – not platform metrics?

In some cases, that process has led to the very deliberate choice not to pursue work at all if it wouldn’t deliver meaningful outcomes.

2. Narrowing solution scopes

Many transformation programs lose credibility because they try to do too much at once. Too many initiatives. Too many KPIs. Too little ownership. Embracing Intelligence in this context means narrowing focus – prioritizing the levers that materially shift performance. It often means accepting slower momentum, but in return, you gain credibility and sustainable performance.

3. Championing transparency

Measuring and reporting outcomes continuously (even when results fall short) is key to building trust in an industry shaped by years of technological disappointment. Embracing Intelligence means acknowledging underperformance and adjusting course, not reframing it as success to save face.

Through all of this, intelligence isn’t something that’s bolted on as an afterthought but brings together lived experience, real-world context, deep domain knowledge and an understanding of what, how and where to utilize digital capability in ways that respect the operational, cultural and commercial realities of the mining sector.

How We’re Embracing Intelligence to Build Trust with Our Clients

In a landscape long dominated by “AI-first” rhetoric, at Cyient we take a more grounded approach, recognizing that real progress in mining depends on the interplay between human expertise and artificial intelligence. Deep domain knowledge allows us to challenge assumptions, guide adoption, and partner with operators on technology that works in practice – not just in concept.

Our multi-industry experience brings additional benefits: from aerospace to nuclear, we have a diverse engineering heritage that shapes how we think about risk, reliability and accountability. These considerations, solutions and approaches to intelligence transfer into Mining and Minerals in a way that are relevant and pragmatic.

We know how the mining industry operates, and we know precisely what’s at stake when solutions don’t deliver.

While the recent maturation of AI may have caused some industries to try and rethink whether they are optimizing their use of technology, we believe that taking a human-first and domain-led approach is the right way to leverage AI and deploy it when and where it adds the most value. ‘Human plus AI’ is how Mining and Minerals translates lofty ambitions into safer, more productive, and more cost-effective ways of working.

Replacing Skepticism with Meaningful Change

Mining does not need a visionary or ‘future-focused’ technology narrative. What it really needs is an approach to the application of intelligence – human plus AI – that is informed, applied with judgment, and proven over time. This means scaling less, focusing more, and embedding intelligent engineering enabled by technology – based on evidence and underpinned by aspiration.

Embracing Intelligence in Mining and Minerals is a way of thinking and working that ensures intelligence is never reduced to “AI-first” or the next big technology, but is the convergence of intelligence in all its forms: human, domain, digital, and artificial.

Because only when we stop seeing intelligence as experimentation and start seeing it as a part of how we drive meaningful impact, can we build deeper trust and create a safer, more efficient future for the industry.