Day 5 — Productizing my first AI automation template
When I started building LeadRadar AI, my goal was simply to automate lead discovery for freelancers.
Five days later, I realized that building the workflow was only half of the work.
Today I focused on productization instead of adding new features.
Here's what changed:
- Cleaned the workflow structure
- Improved readability for new users
- Added a second workflow for daily email digests
- Worked on the installation guide
- Prepared bonus resources to reduce setup time
One lesson stood out:
A workflow that works for its creator isn't automatically a product that others can use.
Documentation, onboarding, and a smooth setup experience are just as important as the automation itself.
For those who have sold templates or digital products:
What took you longer than expected during productization?
Great progress. Productizing AI automation workflows is an interesting shift because it turns custom solutions into repeatable assets. A lot of founders discover that the real value is not just building the automation once, but packaging the knowledge, setup process, and proven workflow into something others can deploy quickly.
The biggest challenge will likely be moving from a “template” mindset to a complete solution: clear onboarding, documentation, edge-case handling, and measurable outcomes. Users don’t just want an automation file — they want confidence that it will solve a specific problem without a lot of tweaking.
Excited to see how this evolves. Turning internal systems into products is one of the most promising paths for AI-native businesses.
Thanks, Luis. I really appreciate your thoughtful feedback.
I completely agree. One thing I realized while building LeadRadar AI is that the workflow itself is only part of the product. The harder work has actually been making it easy for someone else to install, understand, and trust without needing my help.
That's why I'm now focusing on documentation, onboarding, real examples, and reducing setup friction rather than just adding more features.
I'm curious—based on your experience building AI automation systems, what's the most common mistake you see when people try to turn internal automations into products?
Great question. One of the most common mistakes I see is focusing too much on the automation itself and not enough on the user journey around it.
A workflow that works internally often depends on hidden context: specific tools, assumptions, manual decisions, or the creator’s experience. Turning it into a product requires making those invisible parts explicit through documentation, onboarding, error handling, and clear expectations.
Another important step is defining the outcome, not just the process. Users usually don't buy an automation — they buy the result it helps them achieve.
LeadRadar AI is a great example of this transition from a working system into a repeatable product. Excited to see how you continue refining the experience!
Feel free to connect with me on Teams: https://teams.live.com/l/invite/FBAk3iOSJkDyS11JQ?v=g1
Hi Luis, thanks for the invitation. I really appreciated your feedback on Indie Hackers. Your point about selling the outcome instead of the automation itself really resonated with me. I'm in the final stages of preparing LeadRadar AI for launch, so your perspective came at the right time. Looking forward to staying in touch.