Overview
An enormous amount of working time goes to moving information between systems: copying data from email into a tracker, summarizing documents into updates, routing requests to the right person. No-code automation platforms have handled the plumbing for years; adding an AI step to that plumbing is what makes automation genuinely useful for messy, human-shaped work. The hard part is not connecting the boxes, it is designing workflows that behave reliably when an AI step sits in the middle of them.
This is a hands-on, practitioner course. It builds skill in the order it develops in practice: first thinking in workflows, triggers, actions, and data flow, and knowing which of Zapier, Make, and n8n fits a given job, then building real AI-powered automations in each platform. The second day is where most courses stop and this one goes deeper: designing AI steps that produce output the rest of the workflow can trust, and handling errors, cost, and maintenance so automations survive contact with reality. The course covers three tools but one set of durable ideas. Every module includes a lab, and each module builds on the one before it.
Who Should Attend
- Operations, marketing, and business professionals who want to automate repetitive work with AI
- Analysts and power users already comfortable with SaaS tools and ready to connect them
- Developers and IT staff who support citizen automation and want to guide it well
Prerequisites
- Comfort working with everyday SaaS tools (email, spreadsheets, chat, CRM or similar)
- Basic familiarity with a generative AI tool such as ChatGPT or Claude
- No programming experience is required, though it helps in the n8n material
What You Will Learn
- Analyze a manual process and design it as an automated workflow
- Choose sensibly among Zapier, Make, and n8n for a given job
- Build AI-powered automations in each of the three platforms
- Design AI steps whose output the rest of the workflow can act on safely
- Handle errors, monitor runs, and keep automation costs under control
- Document and maintain automations so they outlive their creator's attention
Course Outline
Day one: thinking in workflows, building the first three
- Thinking in Workflows
- Triggers, actions, and data flow: the anatomy of every automation
- Spotting automatable work, and what changes when an AI step joins the flow
- Zapier, Make, and n8n: an honest comparison and when to pick each
- Lab: map one real process from your own work as a workflow design
- First AI Automation in Zapier
- Building a Zap: trigger, AI step, action
- Prompting inside a workflow: writing instructions that run unattended
- Passing data into the prompt and using the response downstream
- Lab: build a working Zap that summarizes incoming items and routes the result
- Visual Workflows in Make
- Scenarios, modules, and routers: branching on what the AI found
- Iterating over collections of items
- Transforming and mapping data between steps
- Lab: build a Make scenario that classifies incoming requests and branches on the answer
Day two: power, reliability, and maintenance
- Deeper Control with n8n
- Where n8n earns its complexity: flexibility, self-hosting, and cost at volume
- Calling any API with HTTP nodes, and richer logic between steps
- Rebuilding a familiar workflow to see the tradeoffs directly
- Lab: build an n8n workflow that combines an AI step with a custom API call
- Designing Trustworthy AI Steps
- Prompts that run unattended: strict instructions and structured output
- Validating AI output before the workflow acts on it
- Deciding which actions need a human approval step
- Lab: harden an existing workflow so bad AI output cannot trigger a bad action
- Errors, Cost, and Maintenance
- Failure paths, retries, and alerting when a run goes wrong
- Understanding cost per run and preventing expensive surprises
- Documenting workflows and handing them off
- Lab: add error handling and monitoring to your workflows, then break one on purpose and watch it recover
Extended Version
The three-day version keeps the same gradient and adds depth and a fuller build:
- Multi-step AI agents inside n8n workflows, as a bridge to Introduction to Agentic AI
- Webhooks and custom integrations for systems without prebuilt connectors
- Governance for teams: shared standards, credentials, and review of citizen automations
- A capstone that automates one complete real process from each learner's work, end to end