Does this work for a mixed group of field and office staff?

Producers, farm and production managers, cooperatives, agribusiness teams, and food-supply operations, plus the quality, logistics, agronomy, veterinary, and safety specialists who review AI-assisted work. We group cohorts by role, so field, office, and supply-chain examples match what each person is responsible for.

Does anyone need a coding or data background to keep up?

No. Nobody needs a data-science or programming background to keep up. Familiarity with your own records, planning cycles, documents, or operational workflows is what counts, since the training builds AI skills around work people already understand.

What's the time commitment, and can it work around a season?

Thirteen hours across two days, run either at your site or as live online sessions. It splits into half-day blocks to fit production schedules, seasonal peaks, shifts, and whichever field and office teams can attend.

Will the exercises reflect our own operation?

Yes. Before the training we map your team structure, documents, approved sources, tools, and priority workflows, then adapt the exercises to your operating model without asking for sensitive producer, customer, or commercial data. A cooperative and a farm group need one set of examples. A food processor needs another.

How is pricing set for this program?

Group size, delivery format, customization depth, and location decide the number. None of the four move on their own. No fixed menu exists to read a price from either, which is why we quote per program. Share your teams and goals with us, and a proposal scoped to your operation follows.

What do we walk away with once the sessions end?

You keep the prompt library, record templates, workflow canvases, and the rollout plan built during the sessions. Questions after the fact are welcome any time, and refresher or advanced sessions can be scheduled later if your approved use cases grow or the season changes.

Which AI tools show up in the sessions?

The core skills transfer across tools, practiced on ChatGPT, Microsoft Copilot, and Gemini, and adapted to your own approved enterprise systems and connectivity limits where you already run one.

Can the training let AI make a specialist diagnosis?

The training may use text and images to organize an observation, draft a record, pull approved guidance, or shape a question for a specialist, but it never delivers the diagnosis itself. Agronomic, veterinary, chemical-use, machinery-safety, and food-safety calls stay with qualified people, and any material claim gets checked against its original source before use.

AI Training · Agriculture & Agribusiness

AI Training for Agriculture & Agribusiness

Field notes, production plans, supplier messages, quality records, and logistics reports pile up across agriculture faster than most teams can work through them by hand. Some of it lives in a notebook, some in a spreadsheet, some in a phone photo — and by planning season it all needs pulling together.

This training builds hands-on workflows for putting AI to work on that pile: organizing it, drafting from it, keeping it moving. Agronomic, veterinary, and safety decisions stay exactly where they belong, with the people qualified to make them — the model organizes, it doesn't diagnose.

modules: 6
hours: 13

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Generative AI, in an agricultural context, means software that reads a field note, supplier message, or technical guide. It can return a structured summary or draft report, including a first-pass answer. It has never stood in the field itself. Agriculture runs on observations made in one place and decisions coordinated somewhere else. Field notebooks, greenhouse logs, livestock records, packing lists, supplier messages, customer specifications, quality forms, and shipment updates rarely arrive in one clean system. The model can speed up the drafting and organizing side of that mess. The judgment about a crop or an animal stays with the person who walked the field. Food-safety calls stay there too.

A confident answer about crop stress, animal health, chemical use, machinery, or food safety can still be wrong — a photo can sharpen a question for an agronomist or vet, but it can't stand in for a diagnosis, and specialist decisions stay with qualified people. Producer records, customer terms, and commercial plans stay out of unsanctioned tools, with an offline plan for patchy connectivity. Over two days, producers, cooperatives, agribusiness, and food-supply teams cover field records, planning, agronomy-source retrieval, and communication, linking repetitive steps to approved AI automation workflows where already running, all inside the AI training catalog approach.

Field knowledge lives in five different places

Observations scatter across notebooks, apps, and photos; AI pulls them into structured summaries the farm manager still verifies.

Planning eats into the hours meant for the field

Seasonal plans and buyer reports eat hours meant for the field; AI reuses already-collected info instead of rebuilding from scratch.

Can you trust an agronomy answer without checking the source?

Models can retrieve and compare agronomy guidance fast, but every claim still gets checked against source and the right specialist.

One update, many audiences

Producers, suppliers, and customers need different vocabulary and detail; AI adapts the message without changing the facts underneath.

Connectivity and data ownership set the real limits

Data often belongs to growers, not the business, and connectivity is patchy; adoption needs clear boundaries and an offline fallback.

01
Generative AI foundations for agriculture teams
120 minBeginner
What large language models and multimodal tools do well and where they fail. Agricultural context changes the answer. Participants leave this session able to say, in plain terms, what to try first on a farm, cooperative, agribusiness, or food-supply workflow.
How a language model actually predicts the next word
What hallucination looks like in agricultural records and guidance
Text and image tasks, plus spreadsheet work AI can assist with
Tasks to delegate, decisions that stay with people
A first pilot small enough to fail safely
02
Prompt craft across field and office workflows, including supply work
120 minBeginner
A repeatable prompt structure, covering context, task, format, source, and checks, practiced on records and communications participants already handle. By the end, each person owns a small set of prompts that still work when teams, crops, suppliers, and seasons change.
A reusable prompt template
Rough notes, structured records
Asking for assumptions and missing information, then source references
Controlling tone for producers, suppliers, customers, and managers
One prompt library the whole team can find and use later
03
Field records and plans backed by knowledge retrieval
180 minIntermediate
Daily observations, work logs, production plans, and technical references become AI-assisted summarization and planning flows. Every flow ends with source checking and specialist review. The model organizes the evidence. An agronomist or veterinarian still does the judging. So does the manager who signs off.
Field, greenhouse, livestock, or facility records, summarized for a quick read
Shift and weekly reports, plus seasonal ones built from the same daily notes
Drafting work plans from approved priorities and constraints
Retrieving agronomy knowledge from approved sources, with citations attached
What to do when two guidance sources disagree
A review trail that runs from source material to final document
04
Communication, quality, demand, and logistics support
150 minIntermediate
Text-heavy coordination across suppliers, customers, quality teams, and logistics partners gets rebuilt as practical drafting and analysis support. Participants learn to summarize demand and shipment data, then sharpen the documentation. They escalate exceptions rather than let AI make an operational or safety call.
Supplier requests and purchase summaries, followed by follow-up messages
Customer updates and product information, plus complaint-response drafts
Quality and compliance document preparation from approved records
Demand, order, inventory, and shipment information, summarized in one pass
Delays and shortages flagged for review, along with inconsistencies
Multilingual communication across growers and buyers, with logistics partners included
05
Multimodal observations and data ownership in safe use
120 minIntermediate
Photographs and field observations help teams document change and prepare a sharper question for an expert, but an image model isn't a crop, animal, or food-safety diagnostician. This module sets boundaries for multimodal use, personal and commercial data, connectivity, and approved tools.
Describing visible field or facility observations from images
Multimodal output that preps a sharper question for an expert
Keeping agronomic and veterinary decisions with specialists, including food-safety calls
Data ownership across producers, cooperatives, customers, and vendors
Where a prompt's data actually goes: consumer tools vs. enterprise ones
Designing workflows for intermittent connectivity and offline fallback
06
Adoption and seasonal workflow playbook
90 minAdvanced
Moving from isolated experiments to repeatable use across permanent and seasonal teams, participants pick pilots with clear boundaries, assign who reviews what, and sketch workflows simple enough to survive staff turnover and a busy season.
Picking pilots around bottlenecks
Onboarding seasonal workers with clear templates and examples
Owners assigned for prompts, sources, reviews, and data access
Suitable tasks, connected to approved AI automation workflows
Measuring useful adoption without adding reporting burden
A 90-day rollout plan adapted to the production calendar

What you will learn

Your clean field-note summaries
Seasonal plans from approved records
Retrieve agronomy knowledge from approved sources, then check every material claim against it
Prepare supplier, customer, producer, and cooperative communications in the right tone and format
Build quality, compliance drafts without inventing missing records or approvals.
Summarize demand, inventory, order, and logistics information so people can decide on the exceptions
Use multimodal field observations for documentation and expert-review prep
Every AI workflow respects data ownership and connectivity limits, with specialist authority intact.

Who should attend

Producers and farm owners, including production managers
Producer unions and cooperatives, alongside field advisory teams
Agribusiness operations and procurement staff, plus commercial teams
Food processing, packing, storage, and quality teams
Supply planning, demand, inventory, and logistics teams
Agronomy, veterinary, compliance, and food-safety specialists who oversee AI-assisted work

Production calendars shape the two-day program, usually split into half-day sessions around shift schedules. Run it onsite or live online. Either way, examples get adapted to your operating model and approved data boundaries.

Format: Onsite or live online
Duration: 2 days (about 13 hours, can be split into half-day sessions)
Group size: Up to 20 participants per group
Language: English or Turkish
Materials: Prompt library, record templates, workflow canvases, and exercise workbook
Certificate: Certificate of completion

AI for Food & Beverage Teams6 modules · 13 hoursGenerative AI training for food and beverage teams: SOPs and shift records, HACCP and deviation documentation, supplier specs, and NPD and label-claim research, with the release and hazard calls kept with your people.View training

AI Training for Veterinary Teams6 modules · 10 hoursA veterinary practice writes consultation notes and discharge instructions every day. Owner messages add to the load. This program trains clinical and support staff to draft that text with AI, built around clinician review, and never touches diagnosis, prescribing, or treatment planning.View training

AI in Supply Chain6 modules · 12 hoursDemand and supply planners spend the week building a pre-read nobody has time to build properly. This program teaches the signal synthesis, scenario stress-testing, and disruption-brief writing that pre-read needs.View training

Using AI in Waste & Circularity6 modules · 12 hoursAI-assisted drafting for waste and circularity teams: collection and facility documentation, contamination and customer communication, plus tender and sustainability reporting, with classification and environmental claims kept with qualified specialists.View training

Tell us whether you run a producer or cooperative operation, an agribusiness or a food-supply business. Then show us where the friction is. We'll build the exercises around your calendar and systems, using only the data you're allowed to show us.

Two colleagues updating a workflow board and checking a process change

Does this work for a mixed group of field and office staff?
Producers, farm and production managers, cooperatives, agribusiness teams, and food-supply operations, plus the quality, logistics, agronomy, veterinary, and safety specialists who review AI-assisted work. We group cohorts by role, so field, office, and supply-chain examples match what each person is responsible for.
Does anyone need a coding or data background to keep up?
No. Nobody needs a data-science or programming background to keep up. Familiarity with your own records, planning cycles, documents, or operational workflows is what counts, since the training builds AI skills around work people already understand.
What's the time commitment, and can it work around a season?
Thirteen hours across two days, run either at your site or as live online sessions. It splits into half-day blocks to fit production schedules, seasonal peaks, shifts, and whichever field and office teams can attend.
Will the exercises reflect our own operation?
Yes. Before the training we map your team structure, documents, approved sources, tools, and priority workflows, then adapt the exercises to your operating model without asking for sensitive producer, customer, or commercial data. A cooperative and a farm group need one set of examples. A food processor needs another.
How is pricing set for this program?
Group size, delivery format, customization depth, and location decide the number. None of the four move on their own. No fixed menu exists to read a price from either, which is why we quote per program. Share your teams and goals with us, and a proposal scoped to your operation follows.
What do we walk away with once the sessions end?
You keep the prompt library, record templates, workflow canvases, and the rollout plan built during the sessions. Questions after the fact are welcome any time, and refresher or advanced sessions can be scheduled later if your approved use cases grow or the season changes.
Which AI tools show up in the sessions?
The core skills transfer across tools, practiced on ChatGPT, Microsoft Copilot, and Gemini, and adapted to your own approved enterprise systems and connectivity limits where you already run one.
Can the training let AI make a specialist diagnosis?
The training may use text and images to organize an observation, draft a record, pull approved guidance, or shape a question for a specialist, but it never delivers the diagnosis itself. Agronomic, veterinary, chemical-use, machinery-safety, and food-safety calls stay with qualified people, and any material claim gets checked against its original source before use.

By team

By industry

By tool

AI Training for Agriculture & Agribusiness

Why AI in this industry

Field knowledge lives in five different places

Planning eats into the hours meant for the field