Key takeaways
- Every business carries assets it has stopped questioning: old records, byproducts, abandoned ideas and inherited assumptions.
- Before adopting AI or any new technology, define the specific decision it should improve.
- Know the quality of your information before trusting what a tool concludes from it.
- A test result, a certification and a paying customer answer different questions. Don’t confuse them.
- Start with a small, measurable pilot, not a broad promise of transformation.
Before a mining company invests in another drill hole, it should ask a different question: What have we already overlooked?
After years in mining, I have come to believe every business should ask the same question before its next acquisition, product launch or technology purchase.
In mining, overlooked assets are literal. They include historical exploration records, material discarded by earlier operators and ground explored for one metal but never seriously examined for another. Much of central Arizona, for example, was explored primarily for copper, with gold treated as a byproduct.
In other industries, the equivalents are less visible but just as real: customer data collected for one purpose and never analyzed for another, a product line abandoned under different market conditions, expertise that could serve a new market, or a byproduct of operations treated as a cost.
Artificial intelligence has made that question more practical to answer. Tools that can sort through years of records and surface patterns are now within reach of companies of every size. But the value depends less on the tool than on the discipline behind it. Here are five lessons from my work that I believe apply to any business.
1) Question what you inherited
In my own work, evaluating historic mine material led to an opportunity outside the gold business entirely. Certain silica-rich materials became the basis for a soil amendment sold by Harvest Gold Organics, which is listed by the Organic Materials Review Institute (OMRI) for use in organic production.
The earlier operators were not wrong. They pursued particular products under particular economic and technical conditions. But their decisions were history, not a final verdict on what the material could become.
Every company runs on decisions made by predecessors under conditions that no longer exist. Some remain sound. Others have simply never been revisited. A resource’s value depends partly on the questions we ask about it, and that is as true of a customer list or a patent as it is of mine tailings.
2) Start with the decision, not the technology
In exploration, it is easy to spend money drilling. The harder task is deciding which holes to drill, in what order and on what evidence.
Many companies approach AI the way an undisciplined explorer approaches drilling: they start with the tool and hope it finds something. I would start instead with a specific decision. Which customers should we prioritize? Where are we losing margin? Which products deserve more investment? A clear question gives you a basis for evaluating software and a way to tell whether it actually improved the work.
An impressive output has little value if it cannot show which evidence supports its recommendation or what remains unknown.
3) Know the quality of your evidence
My exploration work combines historical mining records and the recollections of people who know a district with structural geology, geophysics, geochemistry and field mapping. Each source adds information, and each has limits. An old report may contain a valuable observation without enough context to interpret it. A historical sampling program may have measured only what mattered to its operator.
Every business has the same problem in different forms: incomplete customer records, inconsistent sales data, reports built on assumptions no one remembers making. Apparent precision drives expensive decisions. A ranked list or a polished dashboard can look authoritative even when the underlying information varies widely in quality.
Before asking any tool to interpret your data, know where it came from, how it was collected and what is missing. Keep the gaps visible. And make sure the people using the output can examine the supporting evidence and tell an observed result from an inference. Simply organizing your records and documenting their limitations often creates value before you buy any platform.
4) Don’t confuse promise with proof
In mining, a promising composition does not prove a product is suitable, and a potential application does not prove a viable business. At Harvest Gold Organics, laboratory testing and the OMRI listing served different purposes. Testing helped us evaluate the material. The listing addressed its eligibility for organic production. Neither, by itself, established what a customer would pay for.
The same distinctions apply everywhere. A successful pilot, a positive customer survey, an industry certification and a signed purchase order each answer a different question. Describe each one according to what it actually establishes, and make sure your performance claims have their own evidence.
Then ask the questions that separate an idea from a business. Can you deliver it consistently? Can it meet the customer’s specifications every time? Do the economics still work after production and delivery? Those questions should shape development from the beginning, before heavy investment in a capability no customer needs.
5) Start small and measure
So far, my own use of AI has centered on documentation, such as reports and technical write-ups. That has real value in a business with heavy reporting demands. Applying specialized tools to exploration itself has proven harder than I expected at the scale of a smaller company, and I am still evaluating the options.
That experience taught me to distinguish between applications. Faster documentation and better decision-making are different uses of technology, with different standards of evidence.
For any company, I would rather begin with a defined pilot than a broad promise of transformation. Choose a bounded problem, document how your team handles it today and measure whether the technology produces a real improvement. That might mean hours saved, errors caught sooner or better choices about where to focus. The measure should reflect the decision your business actually needs to make.
Look again at what you already have
The capabilities that turn an overlooked asset into a new business are familiar ones: evaluation, testing, quality control, logistics and a clear understanding of the customer. Technology can strengthen each of them, provided the company stays clear about its commercial objective.
The question I would put to any executive is this: Which assumptions about your business have gone untested simply because you inherited them?
Start there. Assemble the evidence, identify the uncertainty and choose tools that help resolve it. Your next opportunity may come from something new. It may also come from a better look at something you already have.
John Owen is the CEO of US American Resources, Inc., a privately held American resources company, and the founder of Harvest Gold Organics.
