This post is an updated version of our 2020 article on commodity management technology. Re-reading it recently, it struck us how the problem of being locked into a CTRM system that doesn’t offer complete business visibility has become so much more urgent now that AI has entered the mix. In the past, fragmented data led to limitations on your operations and frustrations in your workforce, from the trade desk through to every step of the commodity supply chain. Now feeding fragmented data into an AI system limits everything AI can do for you, too.
Commodity Trading Risk Management (CTRM) software was originally used to record transactions after the fact and report on activity. But the commodities business has changed enormously since those original systems were designed. Markets move faster, regulatory requirements are heavier, supply chains are more complex, and the margin for operational error is thinner.
Where traditional CTRM falls down
A CTRM’s core job is to make data accessible and useful. It was originally designed for risk and compliance, with the data also benefiting traders. But when the data processes don’t include the full business, such as letting operations teams know the details of a new contract, or MtM updating when an operator receives new quality data from an inspection, businesses have to work around the system. Teams maintain their own spreadsheets, email chains become a critical part of the process, and data is manually copied between systems. Errors (understandably) happen, there are significant data-sharing delays, and getting report data together becomes a project in itself.
The result is an organisation that can’t understand its own data, and certainly can’t understand it in real-time. Position reports are always stale, decisions are made on incomplete information, and the middle office spends more time reconciling records than managing risk. And supply chain operations teams are working with a limited picture, dependent on others updating each system and spreadsheet as soon as they’ve completed a real-world action.
What Commodity Management Systems do differently
A Commodity Management System, or CMS, extends the valuable aspects of the CTRM across the entire commodity supply chain, from the trade desk through operations, logistics, and finance, ensuring all teams have access to the same complete, live dataset.
When a contract is created in CommOS, Gen10’s Commodity Management System, that data is put to work straight away. It automatically creates virtual lots that populate shipping instructions, generate documents, and feed into P&L reporting, with your virtual inventory updating as the real-world situation changes. Pricing calculations update with a click after inspections, warehouse reports and container packing lists automatically update your inventory records, and your MtM and P&L reports are completely live, updating throughout the day.
Beyond the trade desk, operations teams both see and create some of the most significant gains. Rather than waiting on traders for information, operators work within the same live system. Lots can be viewed by location, filtered to show only unallocated stock, and carry built-in controls that flag over-allocation or incorrect grades before an error is made.
Shipments and schedules are managed in the same environment, with automated quantity population, multi-modal transport options, and document compliance controls that prevent a shipment from being progressed until mandatory documents are attached. Operators can add cost estimates per shipment and contract, and compare actuals against budget, all without leaving the system or chasing a colleague for a figure.
This creates an operation where information flows automatically in both directions. Traders entering contract data trigger notifications for the back office. Operations teams are automatically updating position and MtM reports for traders and risk managers, whilst invoices are automatically generated and sent with a click, and credit lines are automatically updated when payments enter a connected ledger system.
That shared environment, where every action in one part of the business is immediately visible to every other part, is what separates a Commodity Management System from a traditional CTRM. It improves efficiency and can fundamentally change the focus of what people’s day-job entails. Teams who previously spent hours copying data between systems, chasing colleagues for answers, or tracing errors can now focus on the strategic elements of their role that are both more interesting and more valuable to the company.
All [Gen10 users] have said that once they started using Gen10 their working day has become a lot more manageable, a lot easier, quicker, slicker and faster.
Tony Fox 3 | Corrie MacColl
The data environment you build today is the AI strategy you’ll have tomorrow
Almost every commodity trading firm we speak to is exploring AI and a 2026 report found that 98% of commodity trading executives are already seeing value from AI. The same report found that AI is expected to deliver an average 3.34% uplift in trading P&L by next year. For businesses operating on thin margins, that could be a truly transformational figure.
But multiple surveys and reports all highlight data quality, access, and structure as the single biggest barrier to scaling AI adoption. Companies know the AI tools are there and want to be using them, but what stalls progress is the underlying data environment. For commodity trading firms, that problem is compounded by the complexity of the commodity supply chain, where lot data is frequently transformed as cargo moves through its real-world journey, and the digital records need to keep up.
AI cannot build reliable decision support on unreliable foundations. If the data going in is fragmented, stale, or inconsistently structured (spreadsheets wrapped around a CTRM for example), the outputs will reflect that. If AI is surfacing outputs that need to be verified, second-guessed, or explained, then it is not adding value, it is adding another tool that needs to be manually reconciled. A data problem simply can’t be solved by AI alone.
Adding AI on top of a fragmented data environment exposes its problems. The firms who are seeing real, compounding value from AI are those who invested in a connected, real-time data environment first, because that foundation is what allows AI to do something genuinely useful: understand what is actually happening across the business, right now, not what the data said when it was last exported.
What AI looks like when it has the data it needs
NaNi, Gen10’s agentic AI assistant, is built natively into CommOS. This means that she can access all of your business data in a live, structured environment, and even more importantly, that she understands the context of that data.
Because NaNi operates within the same system where all trading, operational, and financial data lives, she can query live positions, flag contract anomalies, and surface exceptions that would otherwise require manual investigation. She can route approvals, trigger workflows, and provide decision support at the point where decisions are actually being made, using the same data (and data permissions) your team members have access to.
This means your AI platform is not a standalone tool sitting next to your CTRM pulling in a data export from this morning, but a capability that works with live data inside the Commodity Management System. And crucially, data it understands.
Because NaNi operates within CommOS, she knows that this shipment is tied to this contract, that this user has this approval authority, that this position update happened three minutes ago. That context, spanning the full commodity supply chain from trade execution to physical delivery, is what separates genuinely useful AI output from a response that sounds plausible but needs to be verified before anyone acts on it. Even data imported earlier the same day can already be out of sync with reality, but NaNi works from what is true right now, across the full operation.
The benefits compound in both directions. A connected data environment makes NaNi more effective. And NaNi, by surfacing insights and automating tasks, creates more time for teams to focus on the work that actually requires human judgement.
The case against adding more pieces to a system that isn’t working
We understand why organisations try to extend their existing CTRM rather than replace it. When a new system promises to solve a pressing problem for one team, there is a rush to get it live now, rather than risk disrupting the entire business to incorporate a comprehensive system.
But point solutions added onto a CTRM don’t connect the commodity supply chain, they just add to the technical debt of the existing system. An integration added to a CTRM that can’t share data cleanly becomes a maintenance burden. Workarounds and data hand-offs have to be managed, documented, and trained. And an AI tool added onto a fragmented foundation will deliver a fraction of its potential, despite how powerful the tool might be.
At some point, the cost of inaction becomes higher than the cost of changing. And with AI raising the stakes on data quality, that point is arriving sooner and sooner.
What this means in practice
The distinction between a CTRM and a Commodity Management System has always mattered. Back in 2020, when we first wrote this article, it was primarily an operational argument about efficiency, visibility, and whether your teams were spending their time on the right things. That is still the case today, but the stakes are now much higher.
AI success in commodity trading will be determined by whether that firm’s data environment can make the tool useful. A static CTRM supported by manual workflows cannot. The connectivity and collaboration between traders, operators, risk teams, and finance happens outside it, in spreadsheets, email chains, and manual processes. That is the gap that an AI tool, however capable, cannot bridge on its own.
A Commodity Management System brings that collaboration inside the platform, connecting every function, every workflow, and every data point into a single live environment. That connectivity is what makes AI genuinely powerful. And for commodity trading firms where speed to market is one of their key competitive advantages, and their institutional knowledge is another, it is where the conversation about technology has to start.
To see how CommOS and NaNi can support your business, book a personalised demo today.
