For agribusiness companies seeking ways to manage their supply chains and grain elevator operations more strategically, with less manual effort, it’s no longer a matter of “if.” The intelligent, autonomous assistants to synchronize trading, logistics and supply chain decisions in real time and to better manage supply chain and operational risks are rapidly maturing from the proof-of-concept phase into viable commercial applications.

A large U.S.-based and globally active agricultural cooperative is among those embracing AI. Its supply chain moves huge volumes of grain, among other products, from point to point, connecting growers to food and feed companies in dozens of markets around the world. This complex undertaking demands sophisticated supply chain management tools. Among the recent additions to the company’s toolbox is a natural language capability that enables traders to simply verbalize trades into their commodity management system without typing or navigating multiple screens.

The natural language tool is part of an autonomous commodity management hub where commodity trading staff can utilize multiple AI agents to convert unstructured deal summaries into structured data that is then fed directly into the commodity management system, fundamentally transforming the deal capture process from manual to autonomous. It’s a critical step forward for an enterprise where timing is everything, and a few lost minutes or seconds can mean missed opportunities or increased risk.

The shift to an autonomous solution is expected to yield substantial benefits to the agribusiness company: a 3% anticipated increase in accuracy; a 20% reduction in traders’ time to complete administrative tasks; and, perhaps most significantly, an anticipated 3% increase in profit margin due to traders gaining more time to focus on analysis and strategic decisions.

And there’s more to come. Capabilities like the natural language tool for traders only scratch the surface of artificial intelligence’s potential to help agribusiness companies automate and reinvent commodity management across the supply chain, down to the grain elevator level and even beyond, to individual farms and fields.

The Grain Supply Chain as Autonomous Enterprise

Intelligent automations and other AI use cases don’t succeed by accident. They need a strong foundation to deliver value. That foundation includes a quality “feedstock”: in this case, fresh, semantically rich, contextual and trustworthy data from across the enterprise. You also need quality, industry-specific data models that bring all the data together so it can be analyzed in a meaningful way and that recognize and support industry-specific roles and personas, whether that’s a team managing grain elevators, their contents and the logistics around them or a team of commodity traders. On top of that, a strong, transparent and security-focused governance program around all data and AI deployments is also a must. Ensuring accountability, transparency, explainability, fairness, safety and security around AI is paramount. And in that regard, the buck always stops with human beings.

With that foundation in place, agribusiness companies can begin to put autonomous, industry-trained AI assistants and agents to work across the grain supply chain operation. (In this case, “assistant” refers to an AI agent whose task is to oversee multiple other AI agents.) While we’re still in the early days of exploring what intelligent capabilities can do within a grain elevator operation and across the broader supply chain, they are beginning to prove their value in a wide range of use cases, including:

  • Connecting elevator data to decision-making across the broader enterprise. With real-time visibility into stock levels, contract commitments, delivery logistics and other factors, companies can make timelier, better-informed production, supply chain and risk management decisions. By connecting operations, trading, planning and risk management with shared intelligence, teams can act quickly, decisively and confidently based on the information at hand. An autonomous system supports a faster, more accurate response to a potential disruption, so if a blizzard is expected to affect a delivery route, for example, it can suggest alternative logistical pathways to meet contractual obligations. This, in turn, informs elevator stock level planning.
  • Improving product quality. Cargill is using intelligent modeling to maximize quality, food safety and efficiency across its soy supply chain in Brazil. In particular, its complex model for grain blending (mixing grains of different moisture levels for optimal outcomes) is helping the company better manage supplies of a perishable product. Meanwhile, a new generation of smart grain analyzers is emerging to provide rapid, accurate, on-the-spot assessments of grain quality, which then inform decisions about pricing, logistics, storage, etc., and can lead to faster processing, less waste and better margins. When connected to a broader enterprise system, these analyzers can yield important data that agribusinesses can use to improve logistics, inventory management and the overall efficiency of their supply chains.
  • Deploying embodied AI — aka robots — to perform autonomous tasks inside grain storage facilities. In China, companies have developed an AI-powered robot that uses lidar and SLAM (simultaneous localization and mapping) to check and move grain to prevent spoilage, mold and collapses. It’s all done remotely, without subjecting human beings to the hazards involved in manual grain bin maintenance. Meanwhile, Cargill is using an AI-enabled robot to conduct approximately 10,000 autonomous inspections per week inside a European multiseed crush and refinery plant, collecting thermal, acoustic and visual data to identify potential equipment issues before failures occur. It’s the kind of use case that could be readily adapted to grain storage facilities.
  • Optimizing logistics. This summer and fall, a series of pilot projects are set to test an AI-powered inbound truck scheduling and yard management system for grain elevators, crush plants, ethanol facilities and fertilizer terminals. The system uses a computer vision-based queue visibility system and AI to count trucks, estimate wait times and present congestion data in real time. It’s part of a new wave of smart commodity transportation capabilities to optimize scheduling, nominations and execution, which, in turn, cuts reaction time, reduces freight costs and improves delivery reliability.

From the grain elevator to the broader grain supply chain, the potential uses for smart automations and AI in agribusiness do indeed stretch as far as the imagination. And in many cases, the catalyst that brings ideas like these to life in actual, viable commercial use cases is the spirit of co-innovation.

I see it firsthand in my own work on the technology side of the agriculture industry, where agribusinesses and technology companies are working collaboratively to develop new ways to address some of the industry’s longest-standing pain points, bottlenecks and business challenges. As rapidly as the innovations are coming, it’s time more agribusinesses discovered what these advances can do for their operations, their supply chains, their customers and their bottom lines.

Simon Neumann is a grain industry expert with more than 15 years in information technology and with over 10 years of experience in the commodity and agribusiness sector. Throughout his career, he has held a variety of customer-facing roles in engineering, solution management and marketing working with global enterprises across various industries including automotive, mining & metals, agribusiness and more. Today he serves as global industry expert for agribusiness and commodity management solutions at SAP.