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I recently built an AI-powered email automation workflow using n8n.
The workflow:
Reads pending customer data from Google Sheets
Uses AI to generate a personalized email subject and body
Sends the email through Gmail
Records the subject, body, date, and time in Google Sheets
Automatically changes the status from Pending to Sent
This project helped me understand how AI agents, APIs, and workflow automation can work together to solve repetitive business tasks.
It was not perfect on the first attempt, and I faced several issues while configuring the AI output and Gmail fields. Solving those problems was the most valuable part of the project.
I’m continuing to learn and build practical AI automation projects.
hashtag#AI hashtag#Automation hashtag#n8n hashtag#AIAgents hashtag#LearningByBuilding
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This is the difference between automation and an AI agent. 🤖
A traditional automation says:
“When X happens → do A → then B → then C.”
It’s predictable, reliable, and perfect for repetitive processes. But what happens when the next step isn’t always the same? That’s where an AI agent comes in.
Instead of following one fixed path, an agent can:
→ Understand the goal
→ Analyze the situation
→ Decide what information it needs
→ Choose the right tool
→ Take an action
→ Check the result
→ Decide what to do next
For example, imagine a lead-generation workflow in n8n.
Traditional automation:
New lead → Scrape website → Find email → Add to CRM → Send email
Every lead follows the same path.
Agentic workflow:
New lead → AI Agent
The agent decides:
🔎 Does this company fit the target profile?
🌐 Should I research the website further?
📊 Is there enough information to qualify the lead?
✉️ Should I generate a personalized message?
⚠️ Is human approval needed before outreach?
The workflow becomes goal-driven instead of purely step-driven.
And this doesn't mean replacing automation.
The most powerful systems combine both:
n8n handles the reliable execution.
AI handles decisions where the path isn't always predictable.
That’s what makes an agentic workflow different from simply adding ChatGPT to an automation.
The future isn't: Automation OR AI
It's: Automation + AI agents + the right tools.
Great visual! "Automation does the work, AI agent decides the work" is probably the clearest way to explain this shift to clients.
I built my own lead generation machine because I moved to Denmark with zero network.
I moved to Denmark.
I didn't know anyone.
I had no network.
And I needed to find companies I could sell automation services to.
I could spend hours manually searching for companies, finding emails, cleaning data, and sending messages one by one.
Or I could automate the process.
So I built a complete lead generation pipeline:
→ 🐍 Python scraper connected to Denmark's public government CVR database
→ 📊 Google Sheets synchronization to normalize and deduplicate companies
→ ✉️ Automated cold email workflow with 10 sector-specific templates using n8n
→ 📱 Daily Telegram reports with full pipeline statistics
→ 💼 Assisted LinkedIn outreach: 10 leads per day + a ready-to-send message
And these are the numbers:
📈 19,334 leads in the database
📧 71% email coverage
🏭 10 sectors
🔢 34 industry codes
✉️ 25 automated emails per day
♾️ ~534 days of outreach without repeating a lead
💰 $5/month total cost — basically just the VPS.
The interesting part isn't the scraper.
It's not n8n.
It's not Telegram.
The interesting part is that a process that used to take hours of manual work can now run almost entirely on its own.
And that's exactly what I want to build for companies:
Find repetitive processes → design the system → automate them → measure the results.
In the video, I break down the entire pipeline, piece by piece.
If you're building something similar, let me know which part you'd like me to break down next. 👇
#Automation #AI #LeadGeneration #n8n #Python #ColdEmail #SalesAutomation #B2BAutomation #Denmark #BuildInPublic
Really impressive system. The part that caught my attention is the automated cold email workflow.
Once you start scaling the volume, keeping deliverability consistent across domains and inboxes becomes just as important as the automation itself. Great build!
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Nice