Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Three months ago, I was drowning in client projects that required constant manual work. Every time a deal closed in HubSpot, someone had to manually create Slack groups, update spreadsheets, and send personalized welcome emails. It was the kind of repetitive work that makes you question why you became a consultant in the first place.
That's when I decided to treat AI not as a magic assistant, but as digital labor that could actually DO tasks at scale. Most people ask AI random questions and expect miracles. But here's what I learned after six months of deliberate experimentation: AI's true power isn't answering questions - it's doing work.
The client I worked with was a B2B startup burning hours on operational tasks that should have been automated. Sound familiar? By the end of our project, we had built an AI-driven automation pipeline that handled everything from deal closing to project setup without human intervention.
In this playbook, you'll learn:
Why most AI automation attempts fail (and how to avoid the common pitfalls)
The 3-layer system I use to build scalable AI workflows
Real implementation strategies across Make.com, N8N, and Zapier
How to choose the right platform based on your team's actual needs
A step-by-step framework for AI automation that works
This isn't theory. This is what actually happened when we stopped treating AI like a novelty and started using it as a growth engine.
Reality Check
What the AI automation gurus won't tell you
Open any LinkedIn feed and you'll see AI automation "experts" promising that ChatGPT will revolutionize your business overnight. The typical advice sounds something like this:
"Just ask AI to automate everything"
"AI will replace all your manual processes"
"Set up one automation and watch the magic happen"
"AI can do anything if you prompt it correctly"
"No-code tools make AI automation accessible to everyone"
Here's the uncomfortable truth: most AI automation projects fail because people expect AI to be intelligent when it's really just a pattern machine. You can't just throw prompts at it and expect business transformation.
The conventional wisdom exists because AI marketing is everywhere, and everyone wants to believe in the one-click solution. VCs are pushing AI narratives, consultants are selling AI dreams, and platforms are promising "AI-powered everything."
But here's where this advice falls short: AI needs specific direction to do specific tasks. It's not magic - it's computing power that equals labor force. The breakthrough happens when you stop asking AI questions and start building systems where AI can actually execute work.
Most businesses waste months trying to automate everything at once, when they should be identifying the 20% of AI capabilities that deliver 80% of the value for their specific operations. That's where real automation begins.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this B2B startup, they had built a solid product and were generating consistent deals through their sales process. But every time they closed a deal, the same manual nightmare began: someone had to create a Slack group for the project, update multiple spreadsheets, send personalized onboarding emails, and coordinate between different team members.
The client was spending 2-3 hours per deal on administrative tasks that should have taken minutes. Worse, as they scaled from 10 to 30 deals per month, this manual process was becoming a serious bottleneck. They needed their team focused on strategy and client work, not project setup.
I'd been experimenting with AI for six months by this point, deliberately avoiding the hype cycle. While everyone was asking ChatGPT random questions, I was testing AI as a tool for actual work execution. The key insight I'd discovered: AI works best for repetitive, text-based administrative tasks.
The startup's challenge was perfect for this approach. Their process was:
Deal closes in HubSpot
Someone manually creates a Slack group
Project details get copied to multiple tracking sheets
Welcome emails get sent with project-specific information
Team members get notified and assigned roles
My first instinct was to use AI to "intelligently" handle this process. I spent weeks building complex prompts that would analyze deal data and make smart decisions about project setup. It was a disaster. The AI made inconsistent choices, couldn't handle edge cases, and required constant human oversight.
That's when I realized my mistake: I was treating AI like a human decision-maker when I should have been treating it as a very capable automation tool that excels at pattern recognition and text manipulation.
Here's my playbook
What I ended up doing and the results.
The breakthrough came when I stopped trying to make AI "intelligent" and started building a system where AI could execute specific, well-defined tasks. Here's the exact framework I developed:
Layer 1: Task Identification
Instead of asking "What can AI automate?" I asked "What tasks involve text manipulation at scale?" For this client, that meant:
Extracting deal information from HubSpot
Generating project names and descriptions
Creating personalized email content
Updating project tracking documents
Layer 2: Platform Selection
I tested the same workflow across three platforms to understand the real differences:
Make.com (Budget Option): Started here because of pricing. The automation worked beautifully until errors occurred. When Make.com hits an execution error, it stops the entire workflow - not just that task, but everything. For a growing startup, this was unacceptable.
N8N (Developer Paradise): Migrated everything to N8N next. The control was incredible - you can build virtually anything. But every small client request required my intervention. The interface isn't no-code friendly, making me the bottleneck in their automation process.
Zapier (The Winner): Finally moved to Zapier. Yes, it's more expensive. But the client's team could actually navigate through each Zap, understand the logic, and make small edits without calling me. Team autonomy was worth the cost.
Layer 3: AI Integration
Here's where most people get it wrong. Instead of building one mega-prompt, I created specific AI tasks:
Project Name Generator: AI analyzes deal data and creates consistent project naming conventions
Welcome Email Writer: AI personalizes template emails with client-specific details
Task List Creator: AI generates initial project tasks based on service type
Team Assignment Logic: AI suggests team members based on expertise and availability
The complete workflow: HubSpot deal closes → Zapier triggers → AI generates project materials → Slack group created → Team notified → Welcome email sent → Project tracking updated. All without human intervention.
The key was treating AI as a digital worker, not a decision-maker. Each AI task had one job, clear inputs, and predictable outputs.
Platform Comparison
Real experience testing Make.com, N8N, and Zapier for the same use case revealed surprising differences in reliability and team adoption.
Task Breakdown
Instead of one complex automation, we built specific AI workers for naming, writing, organizing, and assigning - each with a single, clear job.
Team Autonomy
Zapier's interface allowed the client team to understand and modify workflows without developer intervention - crucial for long-term success.
Failure Lessons
The first attempt failed because I treated AI like a human decision-maker instead of a pattern-recognition tool for text manipulation.
Within 30 days of implementation, the results were dramatic:
Time Savings: Project setup time dropped from 2-3 hours to 15 minutes per deal
Consistency: Every project followed the same setup process, eliminating forgotten steps
Team Focus: Staff could focus on strategy and client work instead of administrative tasks
Scalability: The system handled their growth from 30 to 50+ deals per month without additional overhead
The unexpected outcome was team morale. Removing repetitive work didn't just save time - it made the team more engaged with meaningful work. Project kickoffs became smoother because everything was consistently organized from day one.
Three months later, they expanded the system to handle client onboarding, progress reporting, and project completion workflows. The AI automation pipeline became the foundation for scaling their operations without scaling their administrative overhead.
The client is still using Zapier today because team independence proved more valuable than saving on subscription costs. Sometimes the best tool isn't the cheapest - it's the one your team can actually use.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the top lessons from building AI automation pipelines across multiple client projects:
Start Small, Think Specific: Don't try to automate everything. Pick one repetitive process and perfect it before expanding.
Choose Based on Constraints: Budget is one factor. Team autonomy, reliability, and learning curve matter more for long-term success.
AI Excels at Text, Struggles with Logic: Use AI for content generation, data manipulation, and formatting. Avoid complex decision-making.
Design for Handoff: If you're building automation for clients, choose platforms they can manage without you.
Test Error Handling: The difference between platforms becomes obvious when things go wrong. Plan for failures.
Document Everything: Create clear documentation so team members understand what each automation does and how to modify it.
Measure Time, Not Tasks: Track how much time automation saves, not how many tasks it completes. Time is the real ROI.
The biggest mistake I see is treating AI automation as a technical project when it's really a process improvement project. Focus on understanding the current workflow before building the automated version.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI automation:
Start with customer onboarding workflows - high repetition, clear patterns
Use AI for trial-to-paid email sequences and user engagement scoring
Automate support ticket routing based on content analysis
Build feedback collection and analysis pipelines for product insights
For your Ecommerce store
For ecommerce stores leveraging AI automation:
Automate product description generation and SEO optimization
Build abandoned cart recovery sequences with personalized content
Create dynamic inventory alerts and reorder point calculations
Automate customer service responses for common inquiries