Why Commodity Trading Operations Teams Should Be AI Skeptical

"I'm AI skeptical. I'm an old-school guy."

We hear this regularly from experienced operations professionals. Usually it comes with a slight defensiveness, as if skepticism is something to apologize for. It isn't.

If you've spent twenty years in commodity trading operations, you've seen technology vendors promise transformation and deliver frustration. OCR systems that couldn't handle stamps. Rules engines that broke every time a counterparty changed their invoice format. "Intelligent" automation that required more babysitting than manual processes.

Your skepticism isn't irrational. It's earned.

The Generational Divide Is Real

In many trading operations, there's a visible split. Younger team members, comfortable with automation, are eager to try new tools. Senior operators, who've built their careers on manual processes, are more cautious.

This divide isn't about being "behind the times." It's about having different data sets. Junior staff have seen AI do impressive things in their personal lives. Senior staff have seen AI fail in their professional lives—repeatedly, expensively, and always with a vendor explanation about "edge cases."

Both perspectives are valuable. The enthusiasm of junior staff drives experimentation. The skepticism of senior staff prevents costly mistakes. The worst outcomes happen when either voice dominates completely.

Why Skeptics Catch What AI Misses

Here's something the AI vendors won't tell you: your most skeptical operators are often your best quality control.

An experienced operator who doesn't fully trust the AI will spot-check its work. They'll notice when an extracted quantity doesn't "feel right" for that counterparty's typical shipments. They'll catch when a port code is technically valid but commercially implausible. They'll question results that a more trusting user would accept.

This isn't inefficiency. It's expertise applied appropriately. The operator who's been doing this for twenty years has pattern recognition that no AI can replicate. Their skepticism is a feature, not a bug.

The Pattern of Overpromise

Commodity trading has seen waves of technology that promised more than it delivered. First-generation OCR could digitize typed text but failed on stamps, signatures, and anything handwritten. Rules-based automation worked beautifully in demos but broke in production with every format variation. "Smart" matching systems required so much manual configuration they barely qualified as automation.

Each wave left operations teams more skeptical—and rightly so. The burden of proof has shifted. AI vendors don't get the benefit of the doubt anymore. They have to demonstrate value with your actual documents, not cherry-picked demo data.

What's Actually Different Now

Modern large language models represent a genuine capability shift. Unlike rules-based systems, they can handle format variations they've never seen before. Unlike traditional OCR, they understand context—they know that "MT" next to a number means metric tons, that "B/L" and "Bill of Lading" mean the same thing.

But capability is not the same as trust. Just because AI can process your documents doesn't mean you should trust it blindly. The appropriate response to improved AI is not uncritical adoption. It's informed skepticism: test rigorously, verify results, and require the system to earn your trust over time.

How to Use Skepticism Productively

Rather than fighting the skepticism of experienced operators, leverage it. Make skeptics the testers—they'll find failure modes that enthusiasts miss. Require proof, not promises, by running your own documents through any system you're evaluating. Preserve human oversight so skeptical operators can review AI outputs. And measure before and after, letting the numbers make the case.

How Mercora Addresses This

We built Mercora with skeptical operators in mind. Every extraction is visible and reviewable. Confidence scores highlight uncertain fields. The system doesn't hide its reasoning—it shows you what it found and why.

We don't expect blind trust. We expect you to verify, correct, and question. Your skepticism makes the system better. Every correction refines our accuracy. Every question reveals an edge case we can improve.

The best AI adoption happens when skeptical experts stay engaged—testing, questioning, and ensuring the system earns their trust through consistent performance.

Get in touch to test Mercora with your most challenging documents.