Unlock the AI advantage in commodity trading: Why AI matters
Commodity trading has always operated in complex, fast-moving environments shaped by geopolitics, weather, infrastructure constraints, regulation, and market sentiment. What has changed is the scale and speed of information, alongside growing pressure to improve operational efficiency while managing risk and protecting margins. Traders and operations teams alike are expected to do more, faster, and with greater accuracy.
In this context, Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and AI agents are increasingly seen as practical tools to support better decisions and leaner operations across the trade lifecycle. Used well, they can help traders and operators process information faster, spot patterns earlier, process information at scale, identify patterns earlier, and embed stronger governance across workflows. Yet while interest is high, tangible returns remain mixed. The gap is rarely about ambition or technology; it is about readiness.
This article explores what firms need in place to move beyond AI experimentation and translate investment into measurable business outcomes. Recent research from MIT, suggests that while organisations continue to increase the AI investment, relatively few have achieved significant business impact, highlighting the importance of readiness, integration and execution.
Why AI matters now
AI is increasingly applied across the trade lifecycle to improve how firms process information, make decisions, and operate at scale.
While use cases vary across organisation, most fall into four broad areas:
These capabilities translate into measurable outcomes, explored in the next article.
AI adoption in commodity trading is not linear
AI adoption is rarely linear. Progress is shaped by data availability, business priorities, and existing technology foundations. As a result, firms often operate at multiple levels of maturity simultaneously: some processes remain manual and heavily reliant on human expertise, while others already use advance analytics, machine learning, or GenAI.
The organisations achieving the strongest results are not focused on where they sit on AI maturity spectrum. Instead, they focus on three practical questions:
- Where is manual effort constraining scale or efficiency?
- Where is decision quality limited by data, speed, or visibility?
- Where can AI augment human judgement or strengthen control?
Firms that realise tangible returns focus on these pressure points, applying AI selectively where it delivers measurable impact, rather than progressing through predefined stages.
The foundation first: getting ready for AI
The three questions above help organisations identify where AI can help create value. However, answering those questions is only the starting point. To translate opportunities into measurable results, firms need the right foundation in place. The organisations seeing the greatest success typically focus on five areas:
Partnering for practical AI adoption
Moving from experimentation to production requires more than technology. Firms need a clear understanding of where value exists, which foundations require strengthening, and how AI can be embedded within existing operating models.
The organisations seeing the strongest results take a pragmatic approach-prioritising business outcomes, scaling successful use cases, and maintaining strong governance throughout adoption. In a market where speed, judgement, and control increasingly define competitive advantage, AI readiness is becoming less of a technology question and more of a strategic one.
