Key Takeaways
- AI use cases for ag lending keep judgment and relationship-building firmly in the realm of human workers.
- AI tools turn data to insights faster so ag lenders can serve customers more promptly.
- Instead of existing in one centralized hub, AI tools can exist at the edge, where work happens.
There’s been no shortage of chatter about artificial intelligence (AI) in the last few years. What’s less clear for ag lenders is what the technology might offer them. After all, the work of ag lending is about relationships and trust. Nobody wants to outsource that to a robot.
And indeed, the most successful use cases we’re seeing in the ag lending industry involve AI in a background role. The organizations with the best results are those that are using AI to streamline back-end processes so their LOs can spend more time building relationships with farmers and less on admin work.
Here’s a look at five ways AI is transforming the work of ag lending – without taking over any of the quintessentially human parts of the job.
1. Augmented Judgment: Make Experience a Compounding Asset
Imagine a world where every person in your organization could benefit from the collective wisdom of everyone else. New hires could tap into years of insights and perspective. Long-time employees could better share their niche expertise.
That’s the promise of augmented judgment, and it’s something that AI tools can make possible for ag lenders.
How? By methodically capturing institutional knowledge and then making it hyper-accessible to every employee.
For example, you might start recording transcripts of every conversation: with clients, with prospects, and among internal team members. You might then use these to train a large language model (LLM), along with documents on your internal policies and guidelines, which anyone can access via natural language query.
An LO on their way to talk to a new prospect might ask the model about how other LOs have handled similar conversations.
Or maybe a relationship manager has taken over the accounts of a recently retired colleague. They’re preparing for a meeting and realize they have unanswered questions about a customer’s history with the organization. They can ask the LLM for a summary of their accounts, then follow up with more detailed questions.
The outcome for the ag lending organization is that AI tools help every employee make better judgment calls by giving everyone access to data in a way that mimics near-perfect recall.
This is typical of AI applications in ag lending: they don’t take over any quintessentially human task; they help lending professionals do those tasks even better.
2. TrustOps: Scale the Actions That Build Trust
Trust is a key advantage that ag lenders have over large national banks and even non-specialized regional banks. Because ag lenders are deeply embedded in the agricultural communities they serve and often have relationships that go back multiple generations, it’s hard for anyone to compete on trust.
The right AI tools can strengthen that advantage even further, cementing ag lenders’ role within their community.
The key is to use those tools to automate and scale the micro-actions that build trust.
For example, a relationship manager might use smart glasses to record their conversation with a farmer during a field visit. On the drive back to the office, an AI agent can parse the conversation and call out next steps based on the discussion.
When the relationship manager gets back to their desk, a neat to-do list is ready for them. They can then send any materials they promised and schedule future follow-ups. These simple acts of follow-through do a lot to build and strengthen trust.
And with the support of AI, the relationship manager can focus all their attention on actually doing that work rather than trying to keep track of a to-do list. In this situation, the AI lets ag lenders scale work that was once incredibly manual.
3. Precision Capital: Time Every Outreach Better
Precision agriculture helps farmers manage their land with the help of data. Precision capital gives lenders a similar capability, but with their portfolio. Ultimately, this allows lenders to have much better timing, which can make a substantial difference in their overall success.
Precision capital is fueled by (among other things) geospatial data and event intelligence, which can help lenders predict what their borrowers may need and so serve them better.
For example, after a flood in the region, a lender can see at a glance which of their customers’ properties were affected, then immediately send messages offering support, resources, and recommended next steps.
Customers get the message that their lender has their back, which builds trust for the long term.
Precision capital can also improve prospecting. With access to the right data lenders can, for instance, predict water availability in future years and so decide whether to offer loans for various properties. They might also use that data to advise current customers on when and whether to sell.
Again, what we see is AI making lenders better at the complex work they do. The AI tools are like data-fueled giants; ag lenders who use them can effectively stand on their shoulders to see further and do more.
4. Governance at the Core: Make Transparency and Compliance Default Settings
No ag lender is going to use an AI tool that opens them to regulatory scrutiny. In fact, in this industry, an AI system’s trustworthiness is more important than its performance.
Every AI tool built for the ag lending industry has to be built with transparency and compliance in mind from the start. That means every model, prompt, and decision must be both explainable and auditable.
For example, you might build a tool to review loan applications. That’s a great use case for AI because it involves reviewing a structured set of data against explicit guidelines. That tool would not only be built to comply with all relevant regulations, it would also have human-in-the-loop checkpoints that require an actual human to verify the model’s outputs.
This lets ag lenders stay accountable while speeding up certain (non-customer-facing) tasks that lend themselves well to automation.
5. Agents at the Edge: Build Tech That Fits Your Workflows (Not Vice Versa)
In digitization 1.0, software could help you do your work, as long as you were willing to learn how to use the software (and, in many cases, upload lots of data into it).
With AI tools, that flips: AI agents (which are bots with AI capabilities) function at the “edges” of your organization – i.e., wherever the action is. Take that field visit the relationship manager records with smart glasses. The to-do list the AI agent produces means the relationship manager doesn’t have to do the admin work between customer-facing tasks.
This is a profound change from how most technology works today. Instead of adapting workflows to suit the available software, ag lenders can build agents to streamline the workflows they have.
Ultimately, of course, this will lead to different ways of working as certain tasks are automated and automation changes how LOs do other tasks.
AI Helps Ag Lenders Double Down on What They Do Best
At the core of successful ag lending operations are relationships built on trust. Used right, AI makes it possible to scale the behaviors that build trust so ag lenders can spend more of their time building and strengthening relationships.
For specific ideas on how to get started with AI at your organization or for a look at real-world applications that are already changing ag lending, check out our ebook The Ag Lender’s AI Field Guide: From Strategy to Practical Steps.