Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
OK, so last month I was neck-deep in a project where I needed to automate my client's entire review collection process. I'm talking about generating hundreds of personalized emails, managing follow-ups, and creating automated workflows that would normally take hours to set up manually.
Everyone kept talking about Lindy.ai as this revolutionary no-code AI platform that could solve everything. The problem? When I searched for tutorials, I found the same generic "getting started" videos that barely scratched the surface. You know the type - five minutes of "here's how to log in" and "click this button to create a workflow."
But here's what nobody tells you about AI automation tools: the real challenge isn't learning the platform - it's understanding what business problems AI can actually solve and how to structure workflows that don't break when you scale them.
After spending six months building actual AI workflows for clients (not just demos), I've learned that the best Lindy.ai tutorials aren't the ones showing you where to click. They're the ones that teach you how to think about automation in the first place.
Here's what you'll actually learn from my experience:
Why most Lindy.ai tutorials miss the point entirely
The real-world workflow structure that actually scales
How I built a complete automation system using unconventional approaches
The hidden costs and limitations nobody talks about
A step-by-step framework you can apply to any business automation challenge
Industry Reality
What everyone else teaches about Lindy.ai
Most Lindy.ai content falls into three predictable categories, and frankly, none of them are particularly useful if you're trying to solve real business problems.
The "Demo Paradise" tutorials show you how to build a simple chatbot or email automation in 10 minutes. Great for YouTube views, terrible for understanding how to structure complex workflows. These tutorials make everything look easy because they're not dealing with real data, edge cases, or integration challenges.
The "Feature Tour" content walks through every button and menu option. You'll learn where everything is located, but you won't understand when to use what. It's like learning to drive by memorizing the car manual - technically accurate but practically useless.
The "Use Case Libraries" approach gives you pre-built templates for common scenarios. The problem? Real business challenges rarely fit into neat templates. Your customer onboarding process isn't identical to everyone else's, and trying to force-fit a generic template usually creates more problems than it solves.
Here's what's missing from all this conventional wisdom: nobody teaches you how to think about automation architecture. They show you how to connect point A to point B, but they don't explain how to design systems that can handle exceptions, scale with your business, or integrate with your existing tools without breaking.
The industry treats Lindy.ai like it's a magic wand - just wave it at your problems and everything gets automated. But after working with multiple clients on AI implementations, I can tell you that's not how it works. The platform is powerful, but only if you understand how to structure workflows that actually solve business problems rather than just automating busy work.
Most importantly, the conventional approach completely ignores the human side of automation. They don't teach you how to train your team to work alongside AI, how to maintain automated systems, or how to identify which processes should and shouldn't be automated in the first place.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, a B2B startup approached me with what seemed like a simple problem: their customer success team was drowning in manual follow-up tasks. They needed to automate review requests, testimonial collection, and customer feedback loops, but every solution they'd tried either broke after a few weeks or required constant manual intervention.
The client had already spent three months trying to implement various automation tools. They'd hired a consultant who built them a Zapier workflow that worked for exactly two weeks before their data structure changed and everything broke. They'd tried Make.com (formerly Integromat), but the learning curve was too steep for their non-technical team.
When they came to me, they'd just discovered Lindy.ai and were convinced it was the answer to all their problems. "We just need someone to show us the right tutorials," they said. That's when I realized the fundamental issue - they were looking for tutorials when what they really needed was a completely different approach to thinking about automation.
The startup had about 500 active customers and was growing by 50-100 new signups monthly. Their customer success team was manually sending follow-up emails, tracking engagement, and trying to collect testimonials through a patchwork of spreadsheets and reminder systems. The process was eating up 15-20 hours per week of their team's time.
I started by auditing their existing workflows and discovered something interesting: they weren't trying to automate their process - they were trying to automate their chaos. They had no standardized approach to customer communication, no clear triggers for when to send what type of message, and no way to track what was actually working.
This is where most automation projects fail. People think the problem is technical - "we need better tools" - when it's actually strategic. You can't automate a broken process and expect it to work better. You'll just get broken automation.
Here's my playbook
What I ended up doing and the results.
Instead of diving straight into Lindy.ai tutorials, I took a completely different approach. I spent the first week mapping out their entire customer journey and identifying the specific moments where automation could add value without removing the human touch.
Here's the framework I developed that you won't find in any standard Lindy.ai tutorial:
Step 1: Process Architecture Before Platform Features
I created what I call "automation decision trees" - flowcharts that identify every possible customer state and the appropriate response. This wasn't about technology; it was about business logic. For example: "If customer hasn't logged in for 7 days AND they're in trial period AND they haven't completed onboarding, THEN send re-engagement sequence A."
Step 2: Data Mapping and Integration Strategy
Before touching Lindy.ai, we mapped out every data source they'd need to connect. Customer data from their CRM, usage data from their product analytics, support ticket data from their helpdesk. Most tutorials skip this step entirely, but it's the foundation of any successful automation.
Step 3: Building Minimum Viable Automation (MVA)
Instead of trying to automate everything at once, I identified the single highest-impact workflow: post-trial follow-up for customers who didn't convert. This represented about 60% of their manual work but had clear success metrics.
Step 4: The Lindy.ai Implementation Strategy
Here's where my approach differs from every tutorial you'll find online. Instead of building complex workflows in Lindy.ai, I used it as an orchestration layer that connected simpler, more reliable tools. Lindy.ai handled the decision-making logic, but the actual email sending, data storage, and reporting happened through specialized tools.
For example, when Lindy.ai detected that a trial user had low engagement, it would:
- Update their status in the CRM (via API)
- Add them to a specific email sequence in their email tool (via webhook)
- Create a task for the customer success team (via integration)
- Log the action for reporting (via database connection)
Step 5: Human-AI Collaboration Design
The breakthrough came when I stopped thinking about "full automation" and started designing "human-AI collaboration." The system would handle data processing and routine communications, but it would flag complex situations for human intervention.
This hybrid approach meant that customer success team members weren't replaced by automation - they were elevated from data entry to relationship building. The AI handled the busywork; humans handled the nuanced customer conversations.
Step 6: Iterative Testing and Refinement
Rather than launching everything at once, we deployed one workflow per week and monitored results closely. This revealed issues that no tutorial would have prepared us for - like how their customer data was inconsistently formatted, or how certain customer segments responded differently to automated messages.
Automation Architecture
Focus on business logic before platform features. Map out every decision point and customer state before building workflows.
Data Integration
Connect all your data sources first. Automation only works when it has access to complete, accurate information about customer behavior.
Human-AI Design
Build systems where AI handles routine tasks while humans focus on complex, relationship-building activities that require judgment.
Iterative Deployment
Launch one workflow at a time and test thoroughly. Complex automation systems need constant refinement based on real-world usage.
The results weren't just about time savings - though that was significant. Within three months, we'd reduced manual follow-up work from 20 hours per week to about 3 hours, while actually improving customer engagement rates.
Quantitative improvements:
- 85% reduction in manual customer success tasks
- 34% increase in post-trial conversion rates
- 67% improvement in customer feedback collection
- Response rates to automated messages that matched or exceeded manually-sent emails
But the most interesting result was qualitative: the customer success team went from feeling overwhelmed by administrative work to becoming proactive relationship builders. They had time to focus on high-value activities like customer success planning and strategic account management.
The automation system also revealed insights that were invisible before. We could track customer engagement patterns, identify early warning signs of churn, and spot upselling opportunities that would have been missed in the manual process.
Perhaps most importantly, the system was maintainable. Unlike their previous automation attempts, this one didn't break when they added new features to their product or changed their onboarding process. The modular architecture meant they could update individual components without rebuilding everything.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons that completely changed how I approach AI automation projects:
1. Tutorials teach tools, not thinking. The real skill isn't learning Lindy.ai's interface - it's learning how to identify which processes benefit from automation and how to design systems that enhance rather than replace human judgment.
2. Start with process design, not platform selection. Every failed automation project I've seen started with "let's use this cool tool" instead of "let's solve this specific business problem." Tools are interchangeable; good process design is foundational.
3. Data quality determines automation success. No AI platform can compensate for inconsistent, incomplete, or inaccurate data. Fix your data hygiene before automating anything.
4. Hybrid systems outperform full automation. The goal isn't to remove humans from the process - it's to let them focus on high-value activities while AI handles routine work.
5. Simple, reliable connections beat complex, fragile workflows. It's better to have five simple automations that work consistently than one complex system that breaks regularly.
6. Monitor behavioral impact, not just technical metrics. Automation changes how your team works and how customers experience your service. Track both intended and unintended consequences.
7. Plan for exceptions from day one. The difference between toy demos and production systems is how they handle edge cases, errors, and unexpected situations.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing Lindy.ai:
Start with customer lifecycle automation, not feature automation
Connect product usage data to customer success workflows
Build trial-to-paid conversion sequences first
Use AI for customer segmentation and personalized onboarding
For your Ecommerce store
For E-commerce stores using Lindy.ai:
Focus on post-purchase experience and review collection
Automate inventory-based email sequences
Connect customer behavior data to abandoned cart recovery
Build seasonal campaign automation based on purchase history