Why Commodity Traders Still Need Human Judgment (Even With AI)

The pitch for AI in trade operations often sounds like this: "Automate everything. Remove the human bottleneck. Let AI handle it."

It's a compelling vision. It's also the wrong one.

After dozens of conversations with operations leaders across commodity trading, one principle emerges consistently: human-in-the-loop isn't a limitation of current AI. It's the correct design for critical financial operations.

What "Full Automation" Actually Means

When vendors promise full automation of trade document processing, what they're really describing is one of two things.

The first is automation of the easy cases—the 80% of documents that are clean, well-formatted, and unambiguous. These can indeed be processed without human intervention. But the remaining 20%, the documents with quality issues, unusual formats, or genuine discrepancies, still require human review. "Full automation" just means shifting human effort from routine processing to exception handling.

The second is dangerous overconfidence. Systems that truly process everything automatically, without human checkpoints, will eventually make expensive mistakes. A quantity mismatch that triggers an LC rejection. A pricing error that goes unnoticed until settlement.

Neither version matches what experienced operations teams actually want. They want AI to handle the routine work while preserving human authority over decisions that matter.

Where Human Judgment Can't Be Automated

Some decisions in trade operations require context, relationships, and commercial awareness that AI doesn't have.

Discrepancy resolution is a good example. AI can flag that the bill of lading shows 49,750 MT while the contract specifies 50,000 MT. But deciding what to do about it—accept within tolerance, request a revised document, escalate to the trader—requires understanding the specific counterparty relationship, the commercial context, and the downstream implications.

The same applies to exception handling, commercial decisions about LC presentation, and relationship management with counterparties. How you handle errors matters, and that requires human judgment.

The Augmentation Model

The right mental model isn't "AI versus humans." It's "AI plus humans, each doing what they do best."

AI excels at processing high volumes of documents quickly, extracting structured data from unstructured formats, cross-referencing data points across multiple documents, and maintaining consistent attention across all documents regardless of fatigue.

Humans excel at interpreting ambiguous or incomplete information, making judgment calls that require commercial context, managing relationships with counterparties, handling novel situations that don't match patterns, and taking final accountability for critical decisions.

The optimal workflow doesn't eliminate either contribution. It combines them.

Exception-Based Workflows

The practical implementation looks like this: AI processes all incoming documents. Every email attachment, every uploaded file gets classified, extracted, and matched to the relevant trade. Routine cases flow through automatically—when AI is confident and no discrepancies are found, the document is processed without human intervention. Exceptions surface for human review, and operators make the final decisions.

This model reduces the cognitive load on operations teams dramatically. Instead of reviewing 100 documents per day, they review 15 exceptions. But they retain full authority over the decisions that matter.

Why This Is Safer

Counter-intuitively, systems with human checkpoints are often more reliable than "fully automated" systems. Errors get caught earlier when humans review exceptions. Context gets applied—humans notice when something is technically correct but commercially wrong. Accountability is clear, and trust is maintained with counterparties, banks, and auditors.

How Mercora Addresses This

Mercora is built around human-in-the-loop workflows. Our AI processes documents and flags discrepancies, but humans make the final calls.

The dashboard shows what needs attention—not everything, just the exceptions. Operators see confidence scores, potential discrepancies, and supporting context. They decide whether to accept, correct, or escalate.

We believe that preserving human authority isn't a weakness. It's what makes the system trustworthy enough for critical trade operations.

Get in touch to see how Mercora keeps your team in control.