Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When I first sat down to build AI automation for my ecommerce clients six months ago, I felt like I was drowning in a sea of overhyped promises and technical jargon. Every AI vendor was claiming they could automate "everything" with "just one click." The reality? Most businesses are throwing money at AI tools without understanding what actually needs to be automated.
After spending months experimenting with AI workflows across multiple client projects—from automated review collection to content generation at scale—I've learned that successful AI automation isn't about the technology. It's about understanding your business processes first.
Here's what most consultants won't tell you: AI automation fails 80% of the time because people start with the tool instead of the problem. They get excited about ChatGPT or Claude and try to force it into their workflow, rather than identifying what actually needs automating.
In this playbook, you'll learn:
Why the "AI-first" approach kills most automation projects
My 4-step framework for identifying automation opportunities
How I scaled content creation from 10 to 20,000+ pages using AI workflows
The hidden costs of AI automation nobody talks about
When to use AI vs when to stick with traditional tools
This isn't another "AI will change everything" think piece. This is a practical breakdown of what actually works, backed by real projects and measurable results from ecommerce and SaaS implementations.
Industry Reality
What every AI consultant promises you
Walk into any AI consultancy today and you'll hear the same pitch: "We'll automate your entire business with AI in 30 days." The standard playbook looks something like this:
Audit your current processes - Usually a surface-level questionnaire
Identify automation opportunities - Everything that involves repetitive tasks
Choose the AI platform - Usually whatever they're being paid to promote
Build the workflow - Drag-and-drop interfaces make it look easy
Launch and optimize - "Set it and forget it" mentality
This conventional wisdom exists because it's easier to sell hope than reality. AI platforms want you to believe their tools are magic solutions. Consultants want to package AI as a one-size-fits-all service they can scale across clients.
The problem? This approach treats AI automation like installing software instead of redesigning business processes. Most projects fail within the first 90 days because:
They automate broken processes instead of fixing them first
They don't account for the human oversight AI still requires
They underestimate ongoing maintenance costs
They choose tools based on features, not business outcomes
The industry wants you to believe AI automation is a technology problem. In my experience, it's actually a business process problem that technology can sometimes solve.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, I was facing a problem that would make any freelancer panic. I had an ecommerce client with over 3,000 products across 8 languages, and they needed SEO-optimized content for every single product page. That's 20,000+ pieces of content that needed to be written, translated, and published.
The traditional approach would have taken 18 months and cost more than the client's entire marketing budget. Hiring a team of writers wasn't realistic—they'd need deep industry knowledge about the products, plus SEO expertise, plus multilingual capabilities. Even if I found that unicorn team, the timeline was impossible.
My client was a B2C Shopify store that had grown organically but was getting crushed by competitors with better SEO. They had quality products but were basically invisible online. Their previous agency had created maybe 50 blog posts over two years, which barely moved the needle.
I started with what everyone recommends: I tried hiring freelance writers. Complete disaster. The content was generic, didn't understand the product nuances, and required so much editing that I might as well have written it myself. The writers had SEO knowledge but zero understanding of the industry.
Next, I tried training the client's team to write their own content. Even worse. These were product experts, not writers. They could create one good article per month, maybe. At that rate, we'd need 50 years to finish the project.
Then I tried the "AI will solve everything" approach. I fed ChatGPT some product descriptions and asked it to generate SEO content. The results were laughably bad—generic, repetitive, and missing all the specific technical details that would actually help customers.
That's when I realized everyone was approaching AI automation backwards. Instead of starting with "what can AI do," I needed to start with "what does this business actually need."
Here's my playbook
What I ended up doing and the results.
Here's the framework I developed after months of trial and error with AI automation projects. I call it the SMAP method: Scope, Map, Automate, Polish.
Step 1: Scope the Specific Problem
Instead of "let's automate content," I defined exactly what success looked like: 20,000 unique, SEO-optimized product descriptions that included specific technical details, benefit-focused copy, and proper keyword targeting. Each page needed to feel human-written, not AI-generated.
I spent two weeks just understanding the business. What made their products different? What questions did customers ask? What technical specifications mattered? This wasn't about AI—this was about understanding the business deeply enough to automate it properly.
Step 2: Map the Current Process
I documented exactly how they currently created content: Product manager writes specs → Marketing reviews → Designer adds images → Developer publishes. This process took 3 hours per product and only worked for their top sellers.
The key insight: I wasn't automating content creation. I was automating their entire content workflow. That meant building systems for data extraction, content generation, quality control, and publishing.
Step 3: Automate in Layers
Rather than one big AI workflow, I built four separate automation layers:
Data layer: Exported all product data to CSV for easy manipulation
Knowledge layer: Created a custom knowledge base with industry-specific information and brand guidelines
Generation layer: Built custom AI prompts that combined product data with knowledge base
Publishing layer: Automated upload back to Shopify via API
Each layer was tested independently before connecting them. This made debugging much easier when things inevitably broke.
Step 4: Polish Through Iteration
The first AI-generated content was mediocre. But here's what most people miss: AI automation isn't about perfect output on day one. It's about creating a system you can improve over time.
I spent three weeks refining the prompts, adjusting the knowledge base, and training the AI on the client's specific brand voice. By week four, the AI was generating content that was indistinguishable from human-written copy.
The complete workflow now generates 100 product descriptions per day, across 8 languages, with consistent quality and brand voice. What used to take 3 hours per product now takes 3 minutes.
Process Design
Map your exact workflow before touching any AI tools. Most automation fails because people skip this step.
Knowledge Base
Build a comprehensive knowledge base first. AI is only as good as the information you feed it.
Layer Testing
Test each automation layer independently. It's much easier to debug when you isolate the components.
Quality Control
Set up human review processes. AI automation still needs human oversight—plan for it from day one.
The results speak for themselves, but the timeline was crucial. Month 1: System design and knowledge base creation. Month 2: AI workflow development and testing. Month 3: Full deployment and optimization.
We went from 300 monthly organic visitors to over 5,000 within 3 months. The client's organic traffic increased by 10x, and more importantly, the quality of that traffic improved dramatically.
But here's what surprised me most: the ongoing maintenance was minimal. Once the system was properly designed, it required maybe 2 hours per week to monitor and adjust. The AI workflows became more accurate over time as they processed more data.
The unexpected outcome? Other ecommerce brands started reaching out after seeing the results. What began as a one-off project became a replicable service offering. The automation framework now powers content generation for six different clients.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven key lessons I learned from building AI automation systems that actually work:
Start with process, not technology. Understand what you're trying to achieve before choosing tools.
AI amplifies what you already do. If your current process is broken, AI will just break it faster.
Quality data beats fancy algorithms. Spend more time on your knowledge base than on prompt engineering.
Build for maintenance from day one. AI systems drift over time—plan for ongoing optimization.
Test everything in small batches. Never automate 20,000 pieces of content without testing 20 first.
Human oversight is non-negotiable. AI reduces work, it doesn't eliminate the need for human judgment.
Focus on workflow automation, not just content generation. The real value is in automating entire business processes.
If I were starting over, I'd spend even more time on the knowledge base and less time trying to perfect the AI prompts. The automation framework is only as good as the business understanding that powers it.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement AI automation:
Start with customer support automation before content generation
Focus on lead qualification and onboarding workflows
Use AI to scale personalized email sequences
For your Ecommerce store
For ecommerce stores implementing AI automation:
Begin with product description generation and SEO optimization
Automate inventory management and demand forecasting
Use AI for personalized product recommendations