Attending a Professional Development Day with a presentation by business futurist and AI researcher Gihan Perera prompted me to reflect on my own understanding of AI and consider where farmers are on that journey, sparking this Landline article.
Artificial intelligence is becoming part of everyday farming, with many weighing up how it fits into their operation. It’s shifting from futuristic ideas into practical tools that can support decisions farmers make every day.
The key question is not if AI belongs on farms, but whether you’re ready to use it effectively by considering why, what, how, and when.
Why consider AI?
Farmers face, and continue to face, growing pressures, unpredictable weather, rising input costs, volatile commodity prices, ongoing time and labour shortages and a potentially overwhelming and increasing amount of information and data. AI offers potential practical solutions to these challenges, for example:
- Better forecasting: analysing climate, soil, and yield data to guide sowing, harvesting, and resource use.
- Early problem detection: identifies pests, diseases, or livestock issues sooner for timely action.
- Efficiency and reduced waste: Optimise fertiliser, feed, and irrigation to lower costs and boost productivity.
- Stronger resilience: Provides insights to manage risk, adaptation to extremes, and maintain profitability.
- Time savings: Automates monitoring and alerts, turning data into actionable insights.
- Streamlined decisions: Organises information and compares options to support faster, confident choices.
In short, AI can help make better-informed decisions, work smarter, and respond faster to challenges.
What can AI be used for now?
A range of AI tools are now available for practical on-farm use. They predict yields, pasture growth, feed needs, and financial outcomes; diagnose weeds, pests, or livestock issues via imaging; and alert you to unusual events through sensors. Variable-rate systems and livestock analytics optimise inputs, grazing, and health, while other tools streamline machinery, maintenance, budgeting, and market decisions. AI can also summarise reports, assist with applications, or convert information into audio. These tools complement experience, adding an extra layer of insight to existing knowledge.
How to become AI Ready?
Getting ready to use AI doesn’t require a big overhaul, but a bit of preparation does make a difference. Below are just some of the possible practical tips to start:
- Identify a problem or a challenge you want to solve—whether analysing information or production data, such as optimising spray application or monitoring water points.
- Organise your data, as good data is the backbone of effective and accurate AI, including reliable paddock histories, accurate livestock records, and structured sensor outputs.
- Broaden your learning by reaching out to experienced people who have already adopted or experimented with AI to gain further insight on how to use or even how to start.
- Adapt workflows where AI may change how you operate—using predictive alerts, remote monitoring, or automated reporting.
- Use AI responsibly, by maintaining human oversight, understanding limitations, and ensuring data privacy and security.
- Ensure basic infrastructure such as functional sensors, organised digital records, and adequate connectivity to allow AI tools to perform effectively, in your environment.
- Identify & try tools, such as ChatGPT, Copilot, Google NotebookLM, which offer both free and subscription versions.
When should you start using AI?
As for timing, you don’t need to adopt everything all at once. Most likely you have already engaged with some form of AI — and that’s a start. Starting small is often the most successful route. Consider running a pilot in one paddock or herd to see how a tool performs in real conditions. Others introduce AI seasonally, such as during sowing or irrigation planning, to evaluate its impact at key decision points. Once you’ve built confidence and have the right systems in place, it’s far easier to roll AI out more broadly. Treating adoption as a gradual learning process rather than a single leap helps ensure each step delivers value.
The key is starting early, learning from the first applications, and building capacity over time. AI readiness is a journey rather than a single step. By understanding the purpose of AI, exploring the tools available, preparing your data and systems, and taking a steady approach to adoption, there is great potential to lift efficiency, improve resilience, and make more confident decisions. It’s a practical pathway towards a more productive and adaptable future, while improving outcomes today.
To quote Gihan Perera, AI (Artificial Intelligence) could be considered as a reverse acronym. Think of it more aptly described as IA – an “Intelligence Assistant”.
And in case you’re wondering, yes AI was used to assist me in preparing this article.
