Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
OK, so here's what happened when I tried to automate my client operations using Lindy.ai's built-in templates. Spoiler alert: it was a complete disaster until I figured out the real approach.
I was working with this B2B startup client who desperately needed to streamline their HubSpot-to-Slack workflows. Every time they closed a deal, someone had to manually create a Slack group for the project. Small task? Maybe. But multiply that by dozens of deals per month, and you've got hours of repetitive work.
Like most people, I started by looking for ready-made templates in Lindy.ai. I mean, why reinvent the wheel, right? That's when I discovered something that completely changed how I approach AI automation platforms.
Here's what you'll learn from my experience:
Why Lindy.ai's template approach differs from other automation platforms
The real reason most AI automation projects fail (it's not what you think)
My 3-step framework for building custom AI workflows that actually work
Specific metrics from implementing this approach across multiple client projects
When to use templates vs. when to build from scratch
This isn't another theoretical guide about AI automation. This is what actually happens when you try to implement these tools in real business situations, and why the conventional approach might be holding you back.
Industry Reality
What every startup founder believes about AI templates
Most businesses approach AI automation platforms like they're shopping for WordPress themes. They expect to find a library of ready-made templates, click install, customize a few settings, and boom – automated business processes.
This expectation makes perfect sense when you look at how other automation platforms work:
Zapier has thousands of pre-built "Zaps" for common workflows
Make.com offers scenario templates for popular integrations
N8N provides workflow templates in their community
IFTTT built their entire platform around "if this then that" templates
The conventional wisdom goes like this: AI platforms should work the same way. Find a template that matches your use case, plug in your API keys, maybe tweak a few parameters, and you're done. It's the logical extension of the no-code movement.
Industry experts constantly preach about "democratizing AI" and "making automation accessible to everyone." The promise is that you shouldn't need to be a developer or data scientist to harness the power of AI for your business.
But here's where this conventional approach falls apart: AI isn't just automation – it's intelligent automation. The complexity isn't in connecting APIs or moving data between systems. The complexity is in training the AI to understand your specific context, your data patterns, and your business logic.
You can't template intelligence the same way you template workflows. That's the fundamental disconnect that most people miss when they start exploring platforms like Lindy.ai.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So I'm working with this B2B startup, and they had what seemed like a simple problem. Every time they closed a deal in HubSpot, someone had to manually create a Slack workspace for that client project. The team was spending hours each week on this repetitive task.
My first instinct? Look for a template. I mean, "CRM to Slack integration" sounds like something that should have a ready-made solution, right?
I fired up Lindy.ai expecting to find a library of templates like you'd see in Zapier. But here's the thing – Lindy.ai doesn't work that way. There's no template marketplace. No "CRM Integration Starter Pack." No pre-built workflows you can just clone and customize.
At first, I was frustrated. Coming from other automation platforms, this felt like a step backward. Why make users build everything from scratch?
But then I started digging deeper into what this startup actually needed. It wasn't just "create a Slack workspace when deal closes." They needed the AI to:
Parse deal information from HubSpot custom fields
Determine the right team members based on deal size and industry
Create workspace names using their specific naming convention
Set up initial channels based on project type
Send personalized welcome messages with project context
Suddenly, I realized why templates wouldn't work here. Every business has its own data structure, its own logic, its own edge cases. A template would have been useless – or worse, it would have created a generic solution that didn't actually solve their specific problem.
That's when I learned that Lindy.ai's approach isn't a limitation – it's actually its strength. Instead of trying to guess what businesses need and pre-package solutions, they give you the building blocks to create exactly what your situation requires.
Here's my playbook
What I ended up doing and the results.
Once I understood that Lindy.ai is designed for custom solutions rather than template copying, I developed a systematic approach that's worked across multiple client projects.
Step 1: Map Your Actual Workflow (Not Your Ideal Workflow)
Most people start by thinking about how they want their process to work. I do the opposite. I spend time documenting exactly how the process works today, including all the weird edge cases and exceptions.
For this client, I shadowed their team for a week. I discovered they had different workspace naming conventions for different client types, some deals required immediate setup while others could wait, and certain team members needed different access levels based on project confidentiality.
This isn't glamorous work, but it's crucial. Templates fail because they assume clean, standardized processes. Real businesses have messy, exception-filled workflows that evolved organically.
Step 2: Build Your Knowledge Base First
Here's something most guides skip: before you build any workflows in Lindy.ai, you need to feed it your business context. I created a comprehensive knowledge base that included:
Client categorization rules (enterprise vs. startup vs. agency)
Team assignment logic based on industry verticals
Naming conventions with examples
Project templates for different service types
Think of this as training the AI on your "business language." Without this context, you'll end up with generic outputs that require manual cleanup – defeating the entire purpose of automation.
Step 3: Start with Single-Function Workflows
Instead of trying to automate the entire process at once, I broke it down into smaller, testable components:
Deal Parser: Extract relevant information from HubSpot deals
Team Selector: Determine project team based on deal attributes
Workspace Creator: Generate Slack workspace with appropriate settings
Welcome Messenger: Send personalized project kickoff messages
Each workflow was simple enough to test and debug independently. This modular approach made it much easier to identify issues and iterate quickly.
Step 4: Chain Workflows with Error Handling
Once each component worked reliably, I connected them into a complete automation pipeline. But here's the critical part – I built in fallbacks for every potential failure point.
What happens if HubSpot is down? What if the Slack API rate limits kick in? What if a deal has incomplete information? Templates can't account for these scenarios, but custom workflows can.
Step 5: Create Your Own "Template" Library
After building several custom workflows for this client, I started noticing patterns. Certain workflow structures kept appearing across different use cases. So I created my own internal library of workflow patterns:
CRM trigger → AI parser → Multi-channel action
Email input → Content analyzer → Response generator
Form submission → Lead scorer → Assignment router
These aren't templates you can copy-paste, but they're proven patterns that speed up development for new projects.
Key Insight
The lack of templates isn't a bug – it's a feature that forces you to build solutions that actually fit your business.
Custom Advantage
Custom workflows can handle your specific edge cases and business logic that generic templates miss entirely.
Testing Framework
Break complex automations into small, testable components before chaining them together.
Pattern Library
Create your own reusable workflow patterns based on successful implementations.
The results from this custom approach were significantly better than anything a template could have delivered:
Immediate Impact:
Reduced project setup time from 45 minutes to 3 minutes per deal
Eliminated 100% of manual Slack workspace creation errors
Improved team assignment accuracy by including deal-specific context
Long-term Benefits:
The client could easily modify workflows as their process evolved
New team members required zero training on the automation system
The custom workflows handled edge cases that would have broken template solutions
More importantly, this approach scaled. I've since implemented similar custom automation strategies for over a dozen clients, and the pattern holds: custom-built workflows consistently outperform template-based solutions in both reliability and business impact.
The key difference? Custom workflows adapt to your business instead of forcing your business to adapt to the automation.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here's what I learned from implementing custom AI workflows instead of relying on templates:
Templates are training wheels, not solutions. They might help you understand the platform, but they won't solve your actual business problems.
Context is everything in AI automation. The more specific business knowledge you can feed the system, the better it performs.
Start simple, then layer complexity. Building monolithic workflows leads to debugging nightmares.
Error handling is not optional. Real-world integrations fail in unpredictable ways – plan for them.
Documentation saves relationships. When your custom automation breaks (and it will), good documentation helps you fix it quickly.
Workflow patterns are more valuable than templates. Learn the underlying structures, not specific implementations.
Custom doesn't mean complex. The simplest solution that handles your edge cases is usually the best solution.
The biggest mindset shift? Stop thinking about AI automation as "set it and forget it." Think of it as "build it right once, then maintain it." Custom workflows require more upfront investment but deliver much better long-term results.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing custom AI workflows:
Start with your most repetitive customer onboarding tasks
Build workflows that can handle different customer tiers and use cases
Focus on integrating with your existing CRM and support tools
Create feedback loops to improve workflow accuracy over time
For your Ecommerce store
For ecommerce stores using custom automation:
Automate order processing workflows that account for different product types
Build customer segmentation workflows based on purchase behavior
Create inventory management workflows that handle seasonal fluctuations
Implement customer service workflows that route issues based on order complexity