AI is no longer a future ambition for commodities and energy traders. Many firms have already begun implementing it with a vision to sharpen market insights, make faster and better-informed decisions, and improve risk-adjusted returns. The challenge is that too often it is not implemented in a way that creates a real edge or delivers lasting value.
If you want AI to succeed in your organisation, you need to be asking yourself the right questions before you start. Here are five that, if answered well, will help ensure you implement AI in a way that works in practice, avoids common pitfalls, and delivers measurable results for your trading teams.
1. Education
Change does not start with technology; it starts with your people. If the trading desk does not trust the AI’s signals, it will ignore them. If the risk department does not understand the models, it will block deployment. Without the buy-in of both, your technology will be treated as a side project rather than a core capability.
One of the most effective ways to build this trust is to form a Data & AI Steering Committee with senior traders, risk managers, quants, IT leads, and operations staff. Equip them to understand the benefits AI can bring to their roles, from faster spread identification for traders to improved VaR forecasting for risk. When these individuals become advocates, adoption accelerates because the message comes from trusted insiders.
Ask yourself: Do you have credible, trusted champions for AI adoption inside your trading, risk, and operations teams, and are they equipped to win over the sceptics?
2. Data Foundations
AI will only deliver value if your data infrastructure can handle the diversity and volume of inputs it demands. This includes systems capable of ingesting exchange, broker, weather, shipping, and IoT data without breaking under peak loads, and robust security to protect proprietary trading signals.
Governance and operating models must also be built for speed. As one CDO of a large utility provider told me, in trading, the cost of delay is measured in missed opportunities. Governance should protect the business without slowing it to a standstill.
Ask yourself: Can your data infrastructure handle peak load without compromise, and are your governance processes fast enough to keep opportunities from slipping away?
3. Talent & Teams
The commodities industry is at an inflexion point. Volatility, decarbonisation, and new market entrants are reshaping the sector, making it an attractive prospect for top AI talent. Hiring from outside the industry can inject fresh thinking from sectors where AI is already operating at scale.
But this talent is in high demand. In other industries, top candidates are secured in days, not months. To compete, your hiring process must move at the same pace.
Ask yourself: Are your hiring strategies designed to attract and close top AI talent before they are snapped up by faster-moving industries?
4. The Rise of Data & AI Product Management
The Data & AI Product Manager is becoming a critical role in commodities trading, bridging the gap between the trading desk and technology teams. They ensure solutions are technically sound and commercially relevant, translate trader needs into data science requirements, and make model outputs understandable for the business.
Without this role, misalignment between traders and technology can slow projects and stall adoption. Expect it to become a standard presence in front office teams.
Ask yourself: Do you have a dedicated role ensuring your data and AI initiatives are both commercially relevant and fully adopted by the front office?
5. Governance vs Innovation
The challenge is knowing when to trust a model enough to put it into production. Some desks deploy quickly with a small allocation to test live performance, while others over-test until the market has moved on. The right balance is to address major governance, risk, and compliance concerns first, then roll out AI in small, controlled use cases.
When early deployments deliver results, success stories spread fast. On a trading floor, nothing drives adoption like the fear of missing out on a profitable edge that another desk is using. The reverse is also true. If nothing ever makes it to production, your best AI talent will not stay in an environment where their work never sees the light of day.
Ask yourself: Are your governance processes designed to enable timely deployment, or are they unintentionally slowing innovation and causing opportunities to pass you by?
Getting AI right in commodities trading means more than choosing the right technology. It takes cultural buy-in, strong data foundations, the right team, clear product ownership, and governance that enables innovation. Want to talk through the next steps? Connect with me.
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