Artificial intelligence (AI) isn’t new to agriculture, but it has reached a point where it is no longer limited to research projects or niche tools. What’s driving its growing visibility in cattle health and production is pressure.
Cattle values are high, input costs are higher and small inefficiencies now carry outsized consequences. At the same time, cattle operations are managing more data than ever, often spread across disconnected systems that are difficult to interpret quickly.
AI is emerging as a way to manage that complexity. Not by automating care or decision-making outright, but by processing information continuously and surfacing patterns that would be impractical to track manually. Harold Birch of UnCommon Farms and Robert Terry of Folio3 spoke at CattleCon on how AI could be used to improve how we work on the farm and with animal health.
From Raw Data to Continuous Awareness
A central theme of the discussion was early awareness. AI systems are designed to absorb large volumes of information, learn what “normal” looks like over time and flag changes as they emerge.
“It gives us more insight quicker than we can see with our own eye,” Birch explains. “The AI agent learns from you and gathers information out of your systems and gives it back to you in real time.”
That capability applies broadly — across health signals, operational workflows and financial data.
Rather than relying on episodic review or fixed schedules, AI enables a more continuous view of what is changing within an operation or across herds. This represents a shift from reacting to visible problems toward noticing drift sooner with AI analysis.
Pattern Recognition at a Different Scale
Pattern recognition is one of AI’s core strengths. These systems improve through use, refining their outputs as more data flow through them. They are not static tools; they learn from repeated exposure to real-world conditions.
“AI is not one-and-done,” Terry says. “You put it in place, and it just keeps getting better. It learns from itself — when we put things in place that were 85% accurate and four to six weeks later it’s 99%-plus.”
This adaptation makes it easier to identify subtle trends that might otherwise blend into day-to-day variability. Instead of relying on predefined thresholds alone, AI can recognize deviations because it has learned what typical performance looks like across time, conditions and systems.
Why AI Keeps Coming Back to Economics
Most current AI applications on farms are tied to cost and operational efficiency rather than direct revenue gains. AI speeds up routine work, reduces friction in accessing information and helps identify inefficiencies that quietly accumulate over a season.
“The impacts that we can have in agriculture usually revolve around cost and daily operations,” Birch says. “Most of it has been around the cost components. Things like detecting weeds, detecting sick animals and finding where animals are located.”
For animal health, this economic context shapes how AI fits into advisory roles. Insights that support earlier intervention, better timing or avoided losses tend to resonate more strongly than tools positioned purely around novelty.
Ideas for Where to Start With AI
Birch and Terry emphasize that AI does not need to be adopted perfectly — or all at once — to be useful. Its value often becomes clear through trial, not theory. Practical starting points include:
- Use AI to scan for change — Apply AI to monitor for deviations in health, performance or operations so attention is drawn to what looks different, not just what is scheduled to be checked.
- Summarize before you analyze — Use AI tools to pull together and summarize information from multiple sources before reviews or discussions, reducing time spent searching for context.
- Focus on early signals, not final answers — Treat AI outputs as indicators of where to look first rather than conclusions. Earlier awareness alone can be valuable.
- Reduce repetitive manual work — Experiment with AI for organizing, importing or synthesizing routine information, such as records, reports or metrics, freeing time for higher-level evaluation.
- Apply it where consistency is hardest — AI is especially useful where scale, distance or workload makes consistent monitoring difficult. It can help standardize awareness across people, sites or time.
- Test one workflow at a time — Start small, evaluate whether it improves clarity or efficiency and move on if it doesn’t. Learning what doesn’t work is part of the process.
AI as a Capability, Not a Commitment
Above all, Terry recommends dipping your toe in and seeing what AI can do for you.
“It’s not a spectator sport. When I first got involved with AI, I thought I had to do it perfectly and know a lot. Actually, the best thing you can do is get in and start doing it,” Terry says.
Waiting to understand everything before engaging often means never engaging at all. At the same time, not every tool will be worth keeping, and applying the wrong one can add complexity without benefit.
Rather than a single investment decision, AI is better viewed as a capability to explore. Used thoughtfully, it changes how quickly patterns are noticed, how efficiently information is handled and how confidently decisions can be made. For cattle practice, that shift is what makes AI worth paying attention to.
