Artificial intelligence is already reshaping commodity trading operating models, unlocking digital assets and decentralised finance while redrawing the competitive landscape as new capital flows in and new players enter from outside the sector.
Where is that investment being directed? How deep will this transformation run, and how quickly will it make itself felt?
Speaking to our host Paul Chapman on this episode is Eren Zekioglu. Eren has spent his career across hedge funds and trading houses, working at the intersection of trading, operations and technology, including senior roles at Glencore and Gunvor.
Read below for our key talent impacts from this episode.
Key Talent Impacts
How is the trader's skill set changing with AI?
Commodity trading firms are redefining what good looks like in front-office talent. The progression is moving from engineering backgrounds to Python capability and now towards experience in artificial intelligence and digital assets. Traders are increasingly expected to understand how AI tools generate scenarios, optimise execution and support decision-making, even if final judgement remains human. This represents a structural shift in hiring priorities away from pure market intuition towards hybrid trader-technologist profiles.
What impact will AI have on middle and back office roles?
AI is accelerating the consolidation of traditional middle and back office functions into a single, technology-enabled operating layer. Activities such as risk, P&L, compliance checks, reporting and settlement are increasingly automated and linked directly to execution. This has significant talent implications, reducing demand for large operational teams and increasing demand for fewer, more technically capable professionals who oversee automated processes rather than perform manual tasks.
Why do NOCs have an advantage in AI-driven talent strategies?
National oil companies and newer trading entrants are structurally advantaged from a talent perspective because they are not constrained by legacy systems or entrenched role definitions. They can recruit AI-native talent and design operating models around automation from the outset. This allows them to leapfrog established trading houses and intensifies competition across the sector for digitally fluent traders, technologists and operating leaders.
How are AI and digital assets changing leadership requirements?
AI-led transformation can no longer be delegated solely to CIOs or external consultants. Future COOs, CFOs, CROs and CEOs are expected to understand AI, digital assets and decentralised finance as baseline capabilities. Firms without this fluency at the top risk slow adoption and internal resistance. As a result, demand is rising for senior leaders with experience spanning commodities, technology and digital finance.
How is AI shifting power towards digital-native talent?
The deepest understanding of AI and digital assets often sits with younger employees or technologists who have historically had limited organisational influence. As AI becomes central to trading and operating models, their expertise becomes critical. This is reshaping internal power dynamics, creating cultural tension but also increasing the importance of attracting, retaining and empowering digitally native talent capable of designing and running AI-enabled operating models.
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HC Commodities Podcast Briefing
How is AI already changing the commodities trading operating model?
AI is already embedded across commodity trading, particularly in optimisation, forecasting and process improvement. Rather than replacing people outright, AI is being used to reduce decision time, speed up execution, improve compliance monitoring and automate document processing. This allows trading organisations to scale without a proportional increase in headcount, while improving accuracy and control.
Is AI reducing headcount in energy and commodities trading?
Not in the near term. The podcast makes clear that commodities trading remains relationship-driven and risk-heavy. While AI reduces manual work, there is still a strong need for human judgment, particularly in physical trading. The more immediate impact is a reshaping of roles, especially in middle and back office functions, rather than widespread job losses.
How are trader skill requirements evolving with AI?
Trader profiles are changing rapidly. The traditional progression from engineering backgrounds to Python literacy is now moving towards experience in AI and digital assets. Traders are increasingly expected to understand how AI tools generate scenarios and support execution, even though final decisions remain human-led. This is driving demand for hybrid trader-technologist talent.
Why are national oil companies well positioned for AI adoption?
NOCs are often building trading capabilities from scratch, without legacy systems or entrenched processes. This allows them to design AI-native operating models and recruit digitally fluent talent from the outset. As a result, they may leapfrog established trading houses in both technology adoption and talent strategy.
What does this mean for leadership and talent strategy?
AI adoption must be led from the top. CEOs, COOs and CFOs increasingly need direct fluency in AI, digital assets and decentralised finance. Firms that lack this capability at the C-suite level risk slow adoption and internal resistance. Talent strategies are therefore shifting towards leaders and professionals who can bridge commodities, technology and finance.