From Automation to Innovation: Phase Two and the Long Tail of AI in Commodities Ops

The world is in the midst of an AI revolution; new capabilities have begun to transform nearly all aspects of business. In particular, the commodities world—which is defined by complex physical transactions and a mountain of data that comes in many forms—is seeing many profound changes.

In a previous blog, "Automate the Cumbersome: Phase One of AI Adoption in Commodities Ops", we explored how early adopters benefit from incorporating AI into commodities work streams. In this follow-up article, we examine the transformative power of AI as ops organizations move beyond anticipated tasks and process automations and start to innovate with team enablement, risk mitigation, and the many ways to enhance business outcomes and growth.

These innovations are the long tail of an AI investment. As commodities firms use industry-specific AI to capture and analyze more of their data, the future heralds bigger, accelerating gains in risk reduction and business performance.

AI-Driven Innovation in Commodities Markets

AI is making an impact in many routine aspects of commodities operations including trade confirmations, payment processing, operations intelligence, and inventory management. Broadly speaking, the gains are won in three areas: teams, risk, and growth.

When it comes to teams, AI is driving innovation both in terms of structure, productivity and the ability to handle more involved tasks. Automation of cumbersome rote work frees up employee time and unlocks better data insights, allowing team members to upskill and evolve their roles.

The team at Vitol, a global energy and commodity trading company, was able to move members of its settlements team to focus on natural gas actualization. This upgraded their work from basic processing tasks to outcomes that add value. Whereas the team previously performed routine settlements, the data insights from AI allowed them to explore ops data in much more detail. For example, pricing accuracy was improved by reconciling actual prices with forecasts. Turning to AI gave Vitol’s team increased fidelity of information and more time to consider it, so they could better apply their wisdom.

Looking at it another way, AI increases operational scalability because many routine and repetitive tasks are automated, making it possible for an existing team to handle a greater portfolio of responsibility, effectively increasing their capacity. In a world where efficiency and productivity are eternal goals, AI provides the ability to do more with less and still ensure that operations run smoothly and accurately.

Improving Risk, Driving Growth with AI Tools

This increased efficiency has direct impacts in terms of risk and reward within commodities ops, first through faster and more accurate data processing but also by making it possible to go broader and deeper when it comes to analyzing and understanding complex transactions and relationships.

Freepoint Energy, a U.S.-based commodities trading enterprise, uses AI that was designed to process commodities documents and unstructured data to uncover discrepancies in demurrage charges at a major oil handling facility. Prior to the introduction of the AI tools there was no detailed record of specific deliveries. The new analysis gave a precise timeline of shipments which in turn revealed inconsistencies in demurrage changes. Certain partners were found to consistently invoice for demurrage when other firms making deliveries before or after them were not. Investigation of these inconsistencies not only led to refunds but resulted in permanently reduced costs.

Using AI insights to enhance processes can help save money in ways that are not obvious, or the effects aren’t seen before it’s too late. In one case, an ethanol producer was able to improve the precision of the language used in their numerous complex contracts by using AI to capture and review all of the information in granular detail. Imprecise or inaccurate terms were identified for correction, resulting in fewer opportunities for buyers to attempt to renegotiate an agreement based on a breach of contract terms. This likely saved the company money when the price of distillers dried grains (DDGs) plummeted while the cargo was in transit to Asian buyers. Any contract discrepancies, even minor ones, could have led to a renegotiation and resulted in lower revenue realized.

While avoiding risks is always welcome, just as valuable are opportunities to grow the business. AI tools can create many ways to improve and expand. Two examples from StoneX Commodities Solutions, a leading firm in agriculture markets, highlight some of the ways in which this can be realized.

In the first, StoneX was able to capture greater detail from their existing data and use it in supplier acquisition efforts. Specifically, information on grain and oilseed deliveries used to be binary—either in grade or out of grade—but new AI tools were able to capture greater detail and identify those suppliers that consistently delivered above grade. Merchandisers were able to use that information to procure more business with the premium partners in the future.

In a second example, StoneX was able to use AI to capture new information about suppliers, including the total number of acres that they had in production. Knowing the likely output of these farms, StoneX calculated what percentage of harvest they had been receiving and were able, in turn, to have their customer representatives work to procure more output from those partners with greater capacity and, as in the example above, better quality. By utilizing AI, StoneX was able to grow both the quantify and the quality of their business.

AI is Creating a Long Tail of Innovation in Commodities Operations

While the first phase of AI adoption tends to focus on the automation of familiar processes, the second phase is typically more inventive. As organizations start to understand the flexible capabilities of AI, they become increasingly curious about the possibilities.

AI that is built specifically for commodities operations offers particular advantages for organizations that want to innovate. First, data ingestion is tuned for commodities documents, which can be large in volume and contain complex sets of unstructured or semi-structured information. Second, the data captured from documents can be organized into a data lake that is designed for AI analysis. This combination of comprehensive data normalization and an AI-optimized data architecture makes it possible to run natural language queries and even agentic automation, meaning powerful insights and enhanced processes can be created in moments.

In the same way that the adoption of the internet over 25 years ago forever changed industries and so many aspects of business, AI is predicted to have at least the same scale of effects. We remain in the early days of this transformation, but progress is fast and the resulting shifts in competitiveness will be fierce. This makes it vitally important to get started with AI or risk being left behind. Commodities firms can get creative and ride AI’s long tail or be whipped by it. Contact ClearDox to find out how our AI-native solutions can bring automation and stimulate innovation in your ops organization.