AI Guardrails: Helping Commodity Traders Drive Value

Remember the free-for-all when AI was first popular? Teams experimenting with different chatbots and tools, often with no oversight, no integration with core systems, and no clear strategy. That phase is largely over.

When Gen10 CEO Richard Williamson moderated The Art of AI panel at Commodity Trading Week APAC earlier this year, audience polling revealed that 89% of attendees already have an AI strategy in place, are building one, or are planning to. AI is rapidly becoming part of the core technology stack.

For commodity traders, questions around AI integration and governance carry more weight than in most industries. Trading operations are complex, data is proprietary and commercially sensitive, regulatory reporting requirements are strict, and decisions are made at speed on the basis of information that needs to be accurate and timely. The margin for AI error is slim.

This is why effective AI guardrails are essential, and not just as a protective measure. Clear controls are as much a tool for improving how AI operates as they are for protecting the business. Done well, they make the AI more accurate, more relevant, and more useful for the people relying on it.

At Gen10, we see a future where AI and Commodity Management Systems are integrated, ensuring the AI guardrails, and therefore the AI and the teams using it, are at their most effective.

Data security is table stakes

Data security is a core part of any good AI strategy, but it is only one part of the picture. Effective guardrails support your people in getting better use from the AI by ensuring the information is accurate and relevant, whilst the tool is easy to learn and use.

Embedding an AI tool into your Commodity Management System means people aren’t exporting sensitive information to unknown tools. But it also means the data is timely, structured, and accurate. Therefore, the answers are more relevant and more useful, too.

There is a security architecture benefit too. Fewer bolted-together systems mean fewer potential vulnerabilities. Every additional tool can create a new potential cybersecurity risk, and a new data silo. Keeping AI within your core system reduces both.

And if the AI inherits permission controls from the Commodity Management System, it can ensure that each user only queries data they are authorised to use. This makes it safer to give broader access to the AI tool. The guardrail enables wider adoption, so the business can access its benefits across a wider userbase.

Agents built for your actual use cases

One of Gen10’s core principles in building our Commodity Management System, CommOS, is that every client’s business is unique and their CMS workflows need to reflect that. The same applies to AI. Each organisation will have their own AI risk tolerance, governance processes, and set of guardrails, so their AI instance needs to respect and reflect this.

This extends to how the AI understands what people are actually asking. An operator asking for “all open contracts”, may be looking for unallocated contracts, a very different definition to a trader’s “open contracts”.

A well-designed AI tool, built with the right business context and connected to the right data, can interpret queries differently depending on who is asking and what their role requires. This contextual understanding is one of the most valuable guardrails you can build, but it only works when AI and Commodity Management work together.

Governance and validation

Governance is essential, and is another area where integration between AI and Commodity Management creates an advantage. When AI sits inside your core system, every query and every response can be logged. There is a record of what was asked, what data was used, and how the answer was generated. Compare that to attempting an audit trail when people are copying and pasting to an unmonitored external chatbot.

But auditing alone is not enough. Human validation remains critical, and this is especially true in commodity trading. A wrong number, such as a P&L figure, a position, or a margin calculation, is more dangerous than no answer at all, because people act on it. And when AI gets answers wrong, it presents them with conviction.

Building in human controls is also the mechanism through which the AI gets better over time, as users refine outputs based on their expertise to improve accuracy for the whole organisation. That makes it a quality improvement process as much as a compliance one.

If you would like to understand the approach Gen10 is taking in building up the context our AI agents use to reduce these risks, we would be happy to talk it through with you.

Making AI accessible

In CommOS, workflow guardrails help new hires learn your business processes faster by guiding them through tasks with controls to ensure processes are followed and nothing is missed. AI guardrails work the same way. We don’t need traders or operators to be absolute experts on AI, we just need the AI to help them be more efficient in the areas they are already expert in.

AI guardrails can also democratise access across the business. Once again, controls make it safer for the organisation to provide broader AI access, rather than limiting it to highly-trained teams, meaning the business can see a greater ROI sooner.

Cost management

Guardrails also protect your organisation from unexpected and unnecessary AI costs. Your people should not need to understand token pricing, model selection, or caching strategies when they query the AI. These decisions should be handled in the background, a guardrail that protects the organisation and reduces friction for the people using the tool.

And on a related note, guardrails also include knowing when AI is not the right answer. A good AI project team, working between your business and an expert provider, will identify which user requests are genuinely best served by AI and which are actually a matter of better CTRM configuration.

Partners who understand commodities

This is all obviously much more complicated than simply typing a question into ChatGPT. It relies on integrated systems, custom AI agents, well-structured data, and working with people who understand commodity trading deeply enough to build AI that works with all its complexities.

That’s why Gen10 are building this AI functionality directly into CommOS, our Commodity Management System. With over 25 years of commodity management expertise, we work with clients to ensure their AI tools deliver real operational results and clear value from their AI investment.

Book a personalised demo to explore how Gen10’s Commodity Management and AI systems can work for your trading operations.