In this two-part blog series, we explore how AI is developing and what this means for commodity operations.
The accelerating pace of the AI revolution is bringing awesome opportunities to market. The state-of-the-art advances with each passing week and a wave of expected transformation is sweeping across the business landscape. These new solutions promise the power to redefine how work is done and as these new technologies are combined with human skill and experience entirely new outcomes will emerge.
While everyone can agree that AI is “going to change everything”, it’s not always so clear how AI-powered solutions translate into business value right now. For the manager or decision-maker in the field, the potential gains from deploying intelligent applications can be hard to calculate. At this point, evidence from real-world experience reveals that AI led transformation is proceeding in two phases: first automation and then innovation.
The Current AI Solutions Landscape
The public’s imagination concerning AI has been captured with the introduction of large language models like ChatGPT, Claude, and Dall-e but this is just the tip of the iceberg. AI as a discipline stretches back to the 1950s and AI tools like optical character recognition (OCR) were first introduced in the 1970s. The emergence of sufficient computing power has meant that all AI tools and processes—OCR, NLP (natural language processing), ML (machine learning), LLMs, and Agentic AI—are now within easy grasp.
While the power of AI is impressive, the practical application of these technologies can be unwieldy. Similar to the early days of the internet, when HTTP coding skills were necessary to build even the most rudimentary website, much of current AI amounts to a collection of rudimentary tools that require technical proficiency and expertise to be used effectively. This is a stumbling block for existing businesses.
The impact of AI is already being felt, however, particularly when it comes to traditional, incumbent software as a service (SaaS) firms. Powerful AI tools, particularly Agentic AI, which are AI systems designed to act together autonomously without constant human intervention, are a threat to SaaS firms. In response, these firms are bolting AI on to their products in a bid to remain relevant. The largest of these SasS firms will benefit from their large installed bases but the economics of the space will be altered as solutions providers that are AI native, built from the ground up to maximize the potential of the full suite of AI capabilities, offer better solutions.
The Potential of AI for Commodities
What does this mean for commodity operations? The process begins with documents. Physical commodity markets generate an exceptional amount of documents and vast quantities of data—some of it electronic, some on old-fashioned paper, much of it unstructured—must be ingested, the relevant information extracted, normalized, and then organized into a database.
Under existing processes, much of this work is done manually and, even where electronic ingestion is enabled, human-led steps must be taken to check, reconcile, and transfer information between systems. In addition to being slow and error prone, the repetitive nature of the work is both a waste of valuable human resources and can lead to worker burnout and jobs that are little more than data entry.
While the digitization of data from documents is the starting point, with reduced time and expense in the initial ingestion phase, there are bigger gains from normalizing raw data and then delivering it to all areas of the business in order to drive insights. These can be used to populate dashboards or power intelligent applications that streamline operational tasks and automate workflows in the areas of trade confirmations, payment processing, operations intelligence, inventory management, and more.
Importantly, AI tools make it possible to capture and save all of the data contained in a document. With existing processes, it is often common to gather as little as three or four data points from a complex document and not capture the rest of the information but the power and speed make it possible for AI to collect everything. This breadth and depth of information can yield amazing insights when analyzed.
AI native offerings have a distinct advantage when it comes to commodities data because they apply to best technology, whether that is OCR, NLP, ML, or LLMs, for any particular task. In fact, there are multiple varieties of each type of AI and an AI native firm can deliver the best results because they select and string together the best types of AI flavors to deliver superior results.
Real-World Examples of AI in Commodities Markets
Just as a picture is worth a thousand words, a real world example is far superior to a theoretical explanation. Here are three examples of how AI is being used in the commodity industry right now.
- The back office team at Freepoint Europe, a global merchant in physical commodities, is relatively small but it deals with a broad array of complex products and relationships that they interface with on a daily basis. Broad and deep knowledge was required for the job but routine manual tasks ate up valuable time and resources. By deploying an AI solution for their confirmations process, manual work was reduced from as high as 3 hours per day per employee to only 30 minutes, saving over 75 person hours per week.
- Peak processing demands are a strain for any business and this is particularly true in the agriculture sector where transaction volume soars at harvest time. For StoneX Commodity Solutions, a provider of commodities supply chain management and financing, this led to a strain on staff and a backlog of work that impacted customer relationships with delayed or inaccurate payments. With the help of AI-powered document processing and intelligent apps they were able to automatically process between up to 95 percent of all invoices and settlements. Greater accuracy and a more efficient process enabled them to both invoice and pay partners more quickly and accurately while simultaneously relieving internal of mundane work.
- The best technology solutions are those that can be expanded to bigger, more complex challenges as they go. This was the case with Socar, an energy trading company. Beginning with a solution that handled the reconciliation of standard energy transactions, they were able to deploy AI solutions that built from success to success in three stages, culminating in a solution for reconciling complex and often one-off T+1 transactions in one day, a process that previously took up to a week or more to accomplish.
Transform Your Commodities Operations With ClearDox
The world is in the midst of an AI led revolution that promises to deliver dramatic increases in productivity and insights. New tools are already improving operational visibility, profitability, and lowering risk. However, it is still early days and business leaders are often presented with new AI solutions that are long on technology but short on insight and experience. The commodities professionals at ClearDox deliver purpose-built, AI-driven intelligent applications, including CoPilot chat interfaces and agentic automation. ClearDox is AI native and uses AI tools—OCR, NLP, LLMs, etc.—that best fit the task at hand. As a result, the ClearDox approach usually makes an immediate impact on operations and performance. Schedule a time for a demo to learn how ClearDox can boost your operations, often in as little as six weeks.
This is the first in a two-part blog series that examines the ways in which AI is transforming the commodities industry. Look for “From Automation to Innovation: the Long Tail of AI in Commodities Ops” in the weeks ahead.