AI is already changing the way people work, but for many farmers and food businesses it still feels distant, technical, or not meant for them.
We're building something more grounded: practical support, plain-English sessions, and real examples shaped around the jobs that take time, create friction, or leave people staring at a blank page.
No jargon. No hype. No pretending this needs to look like a tech startup to be useful.
This is not about replacing people or forcing technology into farming. It is about helping farming and food businesses understand where AI can genuinely save time, reduce admin, improve confidence, and support better decisions.
Farming and food businesses are under more pressure than ever. More paperwork. More reporting. More duplicated admin. More information to make sense of. Still the same number of hours in the day.
Used properly, AI can help with the practical jobs that take up time: building grazing plans, reviewing feed rations, turning lambing shed tally boards into usable records, turning agronomist recommendations into simple action lists, comparing performance against benchmarks, and helping people get started when they are not sure where to begin.
The point is not to tell farmers how to farm. The point is to give people useful tools that support their judgement and free up more time for the parts of the job that matter.
Not robots. Not buzzwords. Not a glossy “AI transformation” pitch disconnected from the yard, office, sheds, fields or supply chain.
Listening first, testing the water, and building with the industry rather than dropping something on it from the outside.
Where the admin drags, where confidence gets in the way, and where practical examples would help people see the point quickly.
Farmers are central, but this is wider than that. It is for the people and organisations working across food production, supply chains, advice, support and rural delivery.
Farmers, farm managers, family businesses and new entrants who want practical help rather than technical theory.
Processors, retailers, advisers, consultants and agri-food businesses dealing with heavy admin, reporting and coordination.
Groups, networks, colleges, societies, membership bodies and industry organisations that could bring the right people together.
The strongest signal in the project notes is simple: people need to see themselves in it. That means everyday use cases, plain language, and examples that feel recognisable straight away.
Notes, records, induction material, summaries, planning drafts and repetitive office work are often where the first wins show up.
Many businesses are already collecting data. The challenge is turning it into something more useful, clearer and easier to act on.
People do not need hype. They need to see what good looks like, ask better questions, and leave feeling capable of trying it themselves.
Less time fighting admin. More time for animals, crops, people, businesses and the environment.
This is being built by people who understand farming, food systems, behaviour change and practical AI adoption. That mix is the point.
Techosaurus · AI & digital
Scott makes AI and digital tools accessible, practical and human. His strength is helping people understand what is possible without burying them in jargon.
He focuses on confidence, clarity and real-world application rather than theory for theory's sake.
EmpowerAg · farming people
Amy brings deep knowledge of farming people, communication, informal learning and behaviour change, grounded in lived sector experience.
She understands the confidence barriers that shape whether new ideas land well or get ignored.
Traction · food & supply chains
Chris understands food, farming, supply chains and how to turn good ideas into live, workable projects across the sector.
He helps connect the collaboration to commercial routes, relationships and the wider agri-food system.
The early goal is to build enough signal, interest and feedback to shape the first pilots, workshops, sessions and sector examples well.
Find out who is interested, which parts of the sector they represent, and what problems feel most worth solving first.
Use the interest and feedback to define what the first sessions, pilots and examples should actually look like.
Start with things that feel familiar and useful enough that people can see the value quickly and tell others.
Keep it short. The aim is simply to understand who you are, what part of the sector you are in, and whether you are interested for yourself, your business, or a wider group.