AI in ag lending: why the 90-day rule is key

Artificial intelligence is rapidly transforming agricultural lending, as explained by Cameron Burford, Managing Director at Growers Edge. According to his analysis published by agfundernews, lenders are adopting 90-day AI investment cycles to test, evaluate, and scale new tools that improve efficiency, customer relationships, and decision-making in a faster and more flexible way.

Artificial intelligence is no longer a future trend in agriculture finance. It is already reshaping how ag lenders operate daily, from identifying loan opportunities based on crop patterns to improving compliance workflows and responding to weather-related risks. As highlighted by agfundernews, these tools are becoming part of everyday decision-making across the sector.

The most effective implementations share two key traits. They focus on automating back-end processes and follow a 90-day decision cycle, allowing lenders to quickly test and refine solutions. This approach reduces operational friction and gives loan officers more time to strengthen relationships with their clients.

A central idea behind this transformation is that AI should enhance trust, not replace it. Agricultural lenders are deeply embedded in their communities, often maintaining relationships that span generations. If AI systems fail, especially in customer-facing roles, they can quickly undermine that trust. For that reason, the most valuable use cases are those that support human interaction rather than substitute it.

One example is the use of AI during farm visits. With access to real-time data such as loan history, crop performance, and client preferences, loan officers can offer more personalized and informed conversations. This improves the overall customer experience and reinforces the relationship between lender and borrower.

The shift toward AI also changes how investments are evaluated. Traditional technology models required long implementation cycles and multi-year commitments. Even SaaS solutions, which reduced timelines, still relied on annual evaluations. AI introduces a faster and more dynamic model based on 90-day cycles, where applications are built quickly, tested for a short period, and then either scaled, adjusted, or discarded.

This speed reflects the rapid evolution of AI technologies. Capabilities continue to improve while costs decrease, making it more efficient for organizations to experiment frequently rather than wait for perfect conditions. In this context, learning quickly becomes a competitive advantage.

Another important factor is cost and risk. AI pilots are relatively affordable compared to traditional systems, allowing organizations to experiment without large financial exposure. More importantly, early failures provide valuable insights that help shape better solutions in future iterations. This concept of generating “fast data” is becoming essential for organizations that want to stay competitive.

Gaining internal support for AI initiatives requires a shift in mindset. Leaders must understand that AI is not just another technology upgrade but a different way of operating. The transition from long-term investments to continuous experimentation demands flexibility and a willingness to adapt quickly.

At the same time, AI enables lenders to focus on their unique strengths. By leveraging proprietary data and industry expertise, they can develop customized tools that deliver better results and are difficult for competitors to replicate. This creates a strategic advantage in an increasingly data-driven market.

Importantly, adopting AI does not require perfect or fully integrated data systems. Many applications can operate effectively using existing data sources, such as CRM platforms. This allows organizations to start small, implement solutions at the operational level, and improve their data infrastructure over time.

The urgency to adopt AI is growing. According to agfundernews, the industry is moving from an experimental phase toward broader deployment, where AI will become a standard requirement rather than a differentiator. Organizations that delay may find themselves at a disadvantage in both technology and knowledge.

Leading agricultural lenders are already embracing this shift. They are using AI to build more efficient systems, improve customer engagement, and identify new business opportunities. The conclusion is clear: the time to experiment with AI is now, even if the first steps are imperfect.