Automation
Vibe Coding Feels Like Progress. It Isn't.
It was 12 PM on Monday, and I was drained.
What started as building a trading app over the weekend had bled into my work week.
The early versions worked. But now I was staring at a bug with no idea where to start. Every troubleshooting session with AI opened three new questions, and AI would egg me on for more approvals.
What if your business didn't depend on your best day?
No, never ever!
That’s what my daughter tells me when I ask her to write the same letter more than five times.
She doesn’t want to do it. I understand why. I was the same.
Writing was my weakness growing up. I practised it over and over, not because I enjoyed it, but because I had to. It became acceptable. Not great. Just good enough.
My Professor Said One Word. I've Never Forgotten It.
“What does mechanical mean?”
That was the first question his mechanical engineering professor asked on the first day of class.
Answers came quickly from the room. Something about machines. Something repeatable. Something that works according to instructions.
The professor waited. Then he said, in his Bengali accent:
“Mechanical means boring.”
It took years to understand what he meant.
Why Smart Business Owners Don't Automate Their Most Frustrating Task First?
Photo by Google DeepMind
You know you should be doing something with AI, but where do you even start?
Every week brings a new “breakthrough” tool, another case study, or a viral post claiming to have cracked the AI code.
Your competitors are showing off their “AI-powered” workflows.
Your inbox is full of newsletters promising instant automation wins.
Getting Started with AI Automation: A Practical Guide
Getting Started with AI Automation: A Practical Guide
Many businesses know they should be using AI but don’t know where to start. This guide will walk you through identifying and implementing your first AI automation project using Dhandho principles - low risk, high reward.
Step 1: Identify the Right Process
Not all processes are suitable for AI automation. Look for tasks that are:
- Repetitive - Done the same way multiple times
- Rule-based - Follow clear, definable logic
- Time-consuming - Take significant human hours
- Error-prone - Where mistakes are common and costly
- Data-rich - Generate or use structured data
Examples of Good Candidates:
- Customer inquiry responses
- Data entry and validation
- Report generation
- Email categorization and routing
- Invoice processing
Step 2: Measure Current Performance
Before implementing AI, establish baselines: