Thanks to generative AI in agriculture, farm equipment manufacturers can create service bulletins for recalls in just a few hours. Instead of employees taking days to make lists of affected dealers and customers, GenAI can extract this information from a customer relationship management (CRM) system, assemble repair-related technical details for dealers, and identify potential legal or field issues that might arise from a particular defect. This is just one of many examples of the growing number of cases for generative AI and agriculture.
When generative AI in agriculture is part of an agentic workflow, it can operate across departments and connect systems throughout an agribusiness. GenAI adds intelligence and clarity without increasing overhead. It helps product teams analyze feedback faster, operations teams make more accurate forecasts, and sales teams create stronger engagement collateral with less manual work.
While many generative AI for agriculture systems use natural language processing (NLP) to prompt the model, they do more than generate text from a prompt. GenAI models can analyze complex data or be embedded into many kinds of systems to produce a tangible output (for example, turning translated field data into summaries or reports). The list of uses continues to grow. Here’s how agribusiness can make GenAI a success.
What is GenAI for agriculture?
Generative AI refers to machine learning models that generate new content — text, data, summaries, instructions, and simulations — based on patterns from existing data. These models can output product descriptions, workflow guidance, predictive insights, and business documents. GenAI is often based on a foundational model, such as a large language model (LLM). Regardless of the model type, it is trained on large datasets and fine-tuned for specific use cases.
Generative AI in the agriculture industry is used across the entire business lifecycle. Organizations developing AgTech tools, seed products, agri-services, and AgriTech platforms use GenAI to achieve many business goals. Benefits of generative artificial intelligence in agriculture include:
- Accelerated workflows
- Reduced manual labor
- Automated documentation
- Custom outputs for different needs
Agricultural systems produce massive amounts of structured and unstructured data. Sources include sensor streams, field trials, service logs, and weather monitoring equipment. However, this data is typically underused because it requires human interpretation. With GenAI, such data can be automatically interpreted and output into digestible content at a lower cost than human analysis or rule-based system development.
Value from AI and GenAI for an agricultural organization
Source: McKinsey – From bytes to bushels: How gen AI can shape the future of agriculture
GenAI in an agentic workflow
GenAI models become even more powerful when connected. An agentic system connects multiple AI in agriculture components, or agents, into a single, coordinated structure where each agent contributes to a larger business goal. In these systems, GenAI connects outputs between agents, translates data into human-readable language, and helps different parts of the business understand what is happening and why.
Using a seed producer as an example, a typical agentic workflow includes:
High-level agents
- These agents have higher authority and can help define or change the system’s goals as conditions change. They use data on sales, regional conditions, inventory movement, and strategic priorities.
- For example, a high-level agent might be asked to prioritize the delivery of drought-tolerant hybrids to southern regions by early April.
Mid-level agents
- After high-level agents define the goals, mid-level agents turn them into tactical plans to be executed later.
In the case of seeds, mid-level agents might adjust inventory allocation, coordinate delivery routes, and determine the timing of marketing campaigns across growing regions.
Low-level agents
- Low-level AI agents execute the goals defined by the high-level agents in real time.
- These agents could trigger inventory transfers, update customers, or reroute shipments when delays occur or conditions change.
Adding a GenAI agent
When GenAI is part of an agentic workflow, it adds another layer of productivity by:
- Drafting internal updates that summarize system activity
- Translating technical decisions for operations or sales teams
- Generating customer-facing content tied to live events (delayed shipments, weather-based product guidance)
- Serving as an interface between other AI agents and users to clarify logic, provide system statuses, and discover outcomes
GenAI as an agent is a natural addition to systems already in use, such as forecasting models, CRM systems, ERP platforms, and support portals. GenAI adds a flexible layer that helps these systems communicate, adapt, and improve over time without requiring departmental coordination.
Use cases for GenAI in agriculture
Practical applications of GenAI in agriculture
Agribusinesses use GenAI for internal operations, embed it in products and systems, and offer it in customer-facing applications. In some examples of generative AI in agriculture, the solution is a valuable agent in an agentic AI workflow that provides access to additional data sources. These applications demonstrate how generative AI features can support the agricultural industry.
Product development and modeling
R&D teams use GenAI to simulate different conditions and learn how a product behaves. For example, a seed developer can generate synthetic weather data to stress-test new hybrids before real trials in the field.
Technical support and documentation
GenAI reduces the time it takes to create and maintain service manuals. In fact, it can create dynamic service manuals in real time from diagnostic logs. By integrating GenAI with a CRM, manufacturers can quickly create technical documents for specific dealers and customers.
Dealer and partner enablement
Companies with large distribution networks use GenAI to create region-specific training materials and onboarding kits. The GenAI system looks at partner type, product type, and cultural conditions to produce content relevant for dealers and their customers. Partners can select from templates to quickly create the content they need without relying on a central team. Using GenAI at the partner level also allows a company to serve more partners.
Internal training and knowledge capture
Many managers say knowledge-sharing is one of the biggest challenges during growth and through mergers/acquisitions. GenAI helps capture knowledge from data sources like meeting transcripts and usage logs. It can also improve agility or fill knowledge gaps by creating documentation like SOPs and internal guides, and it can be used for training and product improvement. Furthermore, GenAI keeps these materials current so they can be adapted for new roles or regions, reducing the need for documentation staff.
Customer-facing product features
When connected, integrated, or embedded with other agribusiness systems, GenAI improves the user experience. For example, it can create automated crop performance summaries or plain-language reports from sensor data. Because GenAI makes systems more intuitive, it reduces the burden on support teams. Product teams can also analyze captured user input with GenAI to make future improvements.
Forecasting and logistics planning
Weather forecasts are among the predictions that can be improved with generative AI. GenAI can also predict supply chain delays, regional sales trends, and any other trend that could be predicted manually. From forecasting models and operational data, GenAI can generate summaries, provide strategies, or send an alert to users about an upcoming data anomaly.
Personalized marketing campaigns with the help of GenAI
Marketing and communication
Personalization is possible without GenAI, but GenAI makes it easier. Marketing teams use GenAI to create partner- or customer-specific messaging across many different types of communication. This includes region-specific messaging, seasonal campaigns, product updates, and product recommendations based on previous purchases. For example, a seed producer could send promotions for specific products depending on a customer’s growing region.
Usually, GenAI is introduced in one or two workflows and expanded as its uses and value become clear.
Use cases of GenAI in agriculture
Many agribusinesses are seeing benefits from applications of generative AI in agriculture throughout the value chain. Generative AI use cases in agriculture demonstrate how its benefits become stronger when connected to a live data pipeline and other AI models as part of an agentic workflow. Connecting to other systems also extends generative AI’s value. Computer vision, IoT sensors, forecasting models, CRMs, ERPs, controllers, and even SaaS platforms are easier to use with the help of GenAI. Here are some generative AI examples in agriculture.
Scenario modeling with synthetic data
- Bayer Crop Science uses GenAI to simulate how seed hybrids will grow in drought and other weather conditions. Bayer’s FieldView platform combines synthetic weather and soil models to see how crops respond before planting.
- Corteva Agriscience uses GenAI models based on proprietary field and simulated weather data. The company generates simulations to see how seeds will grow with changing environmental variables.
Auto-generating parts replacement guides
- John Deere uses GenAI in its Smart Manuals and Machine Sync platforms to automatically generate step-by-step repair and maintenance instructions based on machine diagnostics.
- AGCO has a GenAI-enhanced documentation system that uses sensor data and service records to suggest parts and automatically create maintenance guides.
- Bosch Rexroth uses GenAI for (among other things) creating technical documentation and parts breakdowns from CAD and service data.
Producing onboarding packages
- Trimble Agriculture uses GenAI to create training materials and onboarding packages based on role, tool use, and task data.
- CNH Industrial (Case IH, New Holland) uses GenAI in their dealer and operator portals to create product and training guides based on equipment specs and user profiles.
Product summaries
- Syngenta has explored the use of generative AI internally to create marketing content and product comparison summaries. These are generated from structured trait databases, allowing for quick creation of region-specific summaries that highlight key pest resistance, environmental fit, and agronomic benefits.
- Nutrien Ag Solutions uses generative AI in its digital ag retail platform to deliver dynamic, data-driven product summaries and recommendations. Summaries and recommendations are tailored using real-time soil data, weather forecasts, and crop input profiles to help farmers make smarter decisions about seed, fertilizer, and crop protection.
Challenges of adding GenAI to agricultural workflows
Despite the seemingly endless benefits, there are many common challenges to adding generative AI in farming and other agribusinesses.
- Data fragmentation: Agricultural data comes from many sources, including machines, sensors, and digital platforms. Establishing a high-quality, unified data source for GenAI improves the success of early use cases.
- Infrastructure limitations: Remote locations, such as agricultural fields, don’t always have internet connectivity, making a constant connection to the cloud impossible. When sensors and other devices can’t send data to the cloud, it creates a gap in the data set. Establishing a hybrid deployment of GenAI or hosting AI models locally ensures that all captured data is stored.
- Limited technical resources: Training AI models is expensive. It requires intense computing power and extensive resources, including personnel. Instead of creating a GenAI model, companies can use third-party APIs that connect to open-source models.
- Explainability and trust: Companies need to know how their GenAI solutions reach their conclusions to be sure that systems don’t contain bias. Citing sources for the output helps build trustworthiness.
- Model drift: Nothing lasts forever, including the training of GenAI models. As regulations, markets, and seasons change, models can become inaccurate. Periodically retraining a GenAI model keeps its answers relevant and correct.
- Privacy and compliance: Agribusinesses should not use GenAI to analyze business-sensitive data without protection. Role-based access and model boundaries must be created before deployment.
- Ethical and operational risks: If historical data is biased, GenAI can unintentionally reinforce the bias. Continuously reviewing the output helps ensure it is fair and correct.
Building smarter agribusiness systems with GenAI
GenAI is evolving into the core component of modern agricultural operations. Whether embedded in forecasting systems, connected to field sensors, or used to create documents in real time, generative AI solutions in agriculture quickly and consistently support agility and cut expenses across the entire agribusiness value chain. Its ability to reduce manual tasks, personalize output, and turn technical data into plain language makes generative AI a practical solution for the growing complexities of agriculture.
Find out how Intellias can design a GenAI system to simplify AgriTech in your environment. Contact us for a consultation.
