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Physical commodity trading is shaped by information that rarely arrives in clean, structured formats. Traders, analysts, and schedulers work across long email threads, PDF attachments, unstructured reports, shifting vessel updates, and bespoke contract clauses. Most operational workflows (from intelligence gathering to logistics coordination) still rely heavily on text, manual interpretation, and repeated communication.
Meanwhile, information volume is expanding faster than teams can process: more market commentary, more regulatory documentation, more operational messages, more fundamentals from more sources. The challenge is not access to data, but the ability to synthesise it quickly and consistently.
This is why Generative AI has entered the conversation. It is not a replacement for expertise – rather, it excels at filtering noise, reorganising unstructured content, and accelerating synthesis at a pace humans cannot match manually. Used responsibly, GenAI reduces cognitive load and helps teams reach clarity sooner.
To understand where GenAI brings real, practical value, we must first examine where unstructured information creates friction long before and long after a trade is executed.
Physical commodity trading involves far more than discovering a price and executing a position. Daily work unfolds in an ecosystem dominated by unstructured communication: emails, PDFs, shipping notices, document amendments, operational alerts, and internal commentary. These inputs are accurate but fragmented, inconsistent, and ever-growing.
A single cargo can involve dozens of email threads: nominations, ETA updates, diversions, laycan adjustments, credit checks, quality clarifications. Each stakeholder (traders, schedulers, operators, risk) often sees only part of the full picture.
Contracts require repeated interpretation; shipping documents vary by region and counterparty; market intelligence is dispersed across providers; operational messages arrive continuously as conditions shift.
These workflows are not broken, but they are simply text-heavy and dependent on manual intervention, which introduces natural bottlenecks:
This is exactly where GenAI provides the earliest value: in the pre-trade phase, where teams process the highest volume of unstructured information before any trade is placed.
Before a trade is executed, teams face a practical challenge: identifying what matters, and doing so quickly. Pre-trade is the moment of highest information pressure; clarity is far more valuable than completeness.
GenAI fits naturally here. Not because it changes decision-making, but because it removes much of the manual conversion work needed before decisions can happen.
Pre-trade is not about volume; it is about clarity. GenAI shortens the path from raw commentary to decision-ready insight.
Once pre-trade clarity is established, the focus shifts from market interpretation to real-time action. Execution requires fast decisions rooted in a reliable context. Traders navigate price moves, operational constraints, counterparty behaviour, and shifting spreads, often under time pressure.
GenAI does not form prices or take positions. Instead, it t strengthens and partially automates the workflows surrounding decision-making, reducing ambiguity and accelerating context-building.
Execution relies on speed, but speed without context increases risk. GenAI reduces the cognitive effort required to assemble fragmented information, enabling experts to focus on judgment, pricing, and timing.
Once a trade is executed, attention shifts from markets to movement – vessels, storage, documentation, and settlement. Post-trade work is driven by coordination and verification, not price formation.
It is also the phase that generates the largest volume of unstructured text: nominations, confirmations, shipping documents, operational alerts, and settlement statements.
GenAI improves accuracy, pace, and consistency across these workflows.
Post-trade is about coordination and accuracy and GenAI streamlines the flow of operational information while keeping experts in control.
Explore how Generative AI supports commodity trading workflows.
As GenAI becomes embedded across pre-trade, execution, and post-trade, one dependency becomes unavoidable: diverse data must be transformed and structured into a form the organisation can consistently understand and work with. Even advanced models cannot overcome inconsistent naming, mismatched units, divergent tenor structures, or unsynchronised timestamps. GenAI does not solve structural data problems; it magnifies them.
Adopting GenAI does not require a disruptive transformation. Effective organizations introduce it gradually, targeting high-impact, low-risk workflows.
This roadmap avoids disruption and strengthens expert judgment.
Successful adopters share four characteristics:
Across the full trade lifecycle, GenAI does not replace analysis and expertise, but it amplifies it. Its value lies in eliminating friction:
Organizations that pair GenAI with strong data foundations and disciplined adoption will gain a meaningful edge: faster insight, more consistent execution, and teams free to focus on commercial judgment rather than manual assembly.
GenAI is a discipline that improves how trading organizations think, communicate, and execute.
Speak with our data experts to explore where GenAI can strengthen your trading workflows.
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