Development & Programming
Online
beginner Level
Foundation Course
53% OFF
AI Automation Foundations
Starts 28 September 2026•8 weeks
Overview
About this programme
Put AI to work on the jobs your team does by hand. AI Automation Foundations teaches you to map a manual process, wire the tools together, and ship automations that run without you — no programming background required.
Curriculum
Programme structure
Prerequisites
Comfort using a computer and everyday business tools such as spreadsheets and email. No programming background required. A laptop and an internet connection; access to the automation and AI tools is provided or guided.
What you'll learn
- Judge which manual processes are worth automating, and which are not
- Write prompts that produce reliable, structured output
- Build multi-step workflows with triggers, branching and error handling
- Connect systems together using APIs, webhooks and shared data
- Stand up an assistant that answers from your own documents
- Run an AI agent with a human approving the consequential steps
- Automate one real business process end to end and hand it over
7 modules
- What today's AI can and cannot be trusted with, and how to tell the difference before you build. Map a process you do by hand, find the parts worth automating, and set the standard the finished automation has to meet.
- What a large language model actually does, in plain terms
- Where AI is reliable, where it is not, and how to tell
- Mapping a manual process end to end
- Choosing what to automate first: frequency, cost, risk
- Defining "done" — what good output looks like
- The tool landscape: assistants, workflows, agents
- Cost, data and privacy considerations before you start
You'll build: a map of a manual process, scored for what is worth automating first
Module 1: How AI Automation Works
What today's AI can and cannot be trusted with, and how to tell the difference before you build. Map a process you do by hand, find the parts worth automating, and set the standard the finished automation has to meet.
- What a large language model actually does, in plain terms
- Where AI is reliable, where it is not, and how to tell
- Mapping a manual process end to end
- Choosing what to automate first: frequency, cost, risk
- Defining "done" — what good output looks like
- The tool landscape: assistants, workflows, agents
- Cost, data and privacy considerations before you start
You'll build: a map of a manual process, scored for what is worth automating first