AI & Automation
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Here's what everyone gets wrong about AI content: they think the problem is that it sounds robotic. That's not the real problem.
The real problem is that most people are using AI like a magic 8-ball – asking it random questions and hoping for the best. Then they wonder why their content reads like it was written by someone who's never actually worked in their industry.
After generating over 20,000 SEO pages across 4 languages for various clients, I've learned that the difference between AI content that gets penalized and AI content that ranks isn't about avoiding AI – it's about using AI intelligently.
Google doesn't hate AI content. Google hates generic, unhelpful content. The same way they penalize content from human SEO writers who don't understand the topic they're writing about, they'll penalize lazy AI content.
In this playbook, you'll discover:
Why most AI content strategies fail (and it's not what you think)
My 3-layer system for creating AI content that sounds authentically human
How I scaled one e-commerce site from <500 to 5,000+ monthly visits using AI-generated content
The exact workflow I use to maintain quality at scale
When AI content works (and when you should avoid it)
This isn't about tricking Google or hiding the fact that you're using AI. This is about using AI as a tool to create genuinely valuable content that serves your audience.
Industry Reality
What the SEO world is getting wrong about AI content
Walk into any SEO conference today and you'll hear the same tired debates about AI content. The industry has split into two camps:
Camp 1: "AI is the devil" – These folks preach that Google will penalize any AI-generated content. They're still manually writing every meta description and treating AI like it's going to destroy their careers.
Camp 2: "AI does everything" – On the opposite extreme, you have people pushing one-click solutions that promise to generate thousands of pages with a single prompt. Just feed ChatGPT your keywords and watch the magic happen.
Both camps are missing the point entirely.
The first group is living in denial. AI isn't going anywhere, and Google has explicitly stated they don't penalize content based on how it's created. They care about quality and usefulness, not the tools used to create it.
The second group is creating exactly the kind of spam that gives AI content a bad reputation. Generic, templated content that adds no value and serves no real purpose except to try to game search algorithms.
Here's what most "AI content experts" won't tell you: the problem isn't the AI, it's the process. Most people are skipping the most critical steps:
Building actual domain expertise into their AI workflows
Creating brand-specific voice and tone guidelines
Structuring content for both search engines and human readers
Quality control processes that ensure consistency
Understanding when human expertise is still essential
The result? AI content that sounds like it was written by someone who's never actually worked in the industry they're writing about. Because it was.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the project that changed how I think about AI content entirely.
I was working with a Shopify e-commerce client who had over 3,000 products across 8 different languages. They needed SEO content for every product page, collection page, and blog post. We're talking about 20,000+ pieces of content.
My first instinct was to do what every other consultant does: hire writers, create content briefs, and manage a content production team. But the math didn't work. Even with the best writers, this would take months and cost more than most small businesses could afford.
So I did what I always do when conventional approaches don't work – I experimented.
My first AI content attempts were exactly what you'd expect: garbage. Generic product descriptions that could apply to any store selling anything. Blog posts that read like they were written by someone who'd never actually used an e-commerce platform.
The breakthrough came when I realized I was treating AI like a replacement for human expertise instead of a tool to scale human expertise.
Instead of asking ChatGPT to "write a product description for a blue t-shirt," I started asking: "How would an experienced fashion retail expert describe this product to someone who's considering buying it online but can't touch or try it on?"
But even that wasn't enough. The real game-changer was when I stopped trying to use AI out of the box and started building custom systems.
Working closely with the client, I spent weeks digging into their industry knowledge. Not just their products, but their customers, their positioning, their unique value propositions, and most importantly – their voice.
This wasn't about creating better prompts. This was about building an AI system that actually understood their business.
Here's my playbook
What I ended up doing and the results.
Here's the exact 3-layer system I developed that transformed generic AI output into content that sounded like it came from an industry expert:
Layer 1: Building Real Industry Expertise
I didn't just feed generic prompts to AI. I spent weeks building a comprehensive knowledge base from the client's existing materials – their industry-specific documentation, customer communications, and years of accumulated expertise.
For this e-commerce client, that meant scanning through 200+ industry-specific resources from their archives. This became our foundation – real, deep, industry-specific information that competitors couldn't replicate because they didn't have access to this internal knowledge.
Layer 2: Custom Brand Voice Development
Every piece of content needed to sound like my client, not like a robot. I developed a comprehensive tone-of-voice framework based on their existing brand materials and customer communications.
This wasn't just "write in a friendly tone." This was specific guidelines about how they handled technical terminology, how they addressed customer pain points, and even how they structured their sentences.
Layer 3: SEO Architecture Integration
The final layer involved creating prompts that respected proper SEO structure while maintaining the human voice. Each piece of content wasn't just written – it was architected for:
Internal linking strategies that made sense for user flow
Keyword placement that felt natural in context
Meta descriptions that actually enticed clicks
Schema markup opportunities
Content structure that answered real user questions
The Automation Workflow
Once the system was proven, I automated the entire workflow:
Product page generation across all 3,000+ products
Automatic translation and localization for 8 languages
Direct upload to Shopify through their API
Automatic internal linking based on product relationships
Meta tag generation that followed SEO best practices
This wasn't about being lazy – it was about being consistent at scale. Every piece of content followed the same high standards, used the same brand voice, and maintained the same level of expertise.
The key insight? AI isn't a replacement for expertise – it's a way to scale expertise. When you combine human knowledge with AI's ability to apply that knowledge consistently across thousands of pieces of content, you don't just compete in the content game – you dominate it.
Deep Research
Building industry expertise takes time, but it's what separates quality AI content from generic spam. I spent 3 weeks just understanding the business before writing a single prompt.
Brand Voice
Your AI needs to sound like your brand, not like every other AI-generated content piece. Develop specific voice guidelines, not generic "friendly tone" instructions.
System Thinking
Don't just optimize prompts – build systems. The workflow, quality control, and automation architecture matter more than the perfect prompt.
Quality Control
Every automated system needs human oversight. I review samples regularly and adjust the system based on performance, not just set it and forget it.
The results spoke for themselves, but they took time to materialize. This wasn't an overnight success story.
Month 1: We started seeing Google index the new content. Traffic remained flat as the content was still being crawled and evaluated.
Month 2: Long-tail keywords started ranking. We saw gradual increases in impressions and some new keyword rankings, but nothing dramatic yet.
Month 3: This is when things took off. Traffic jumped from under 500 monthly visitors to over 5,000. More importantly, the content was actually helping users – we saw increased time on page and lower bounce rates.
But here's what surprised me most: the content quality improved over time. As I refined the system based on performance data, each new batch of content was better than the last.
By month 6, we had over 20,000 pages indexed by Google across 8 languages. The site was ranking for thousands of long-tail keywords that we never could have targeted with manual content creation.
The real validation came from user behavior. People were actually reading this content, sharing it, and most importantly – converting after reading it. The AI-generated content wasn't just ranking; it was serving its intended purpose.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After scaling AI content across multiple projects, here are the key lessons I've learned:
1. Quality beats quantity every time – It's better to have 100 excellent AI-generated pages than 1,000 mediocre ones. Google's algorithm is sophisticated enough to tell the difference.
2. Human expertise is irreplaceable (but scalable) – AI can't create expertise from nothing, but it can apply existing expertise consistently across unlimited content pieces.
3. Industry knowledge is your competitive moat – Anyone can use ChatGPT, but not everyone has deep industry knowledge to feed into their AI systems.
4. Voice and tone matter more than you think – Generic AI content sounds generic because it lacks a specific voice. Develop clear brand voice guidelines before scaling.
5. The system is more important than the prompt – Perfect prompts won't save a broken process. Focus on building sustainable workflows and quality control measures.
6. Localization isn't just translation – When expanding to multiple languages, cultural context matters as much as linguistic accuracy.
7. SEO and user experience aren't opposing forces – Content can be optimized for search engines while still serving real user needs. The best AI content does both.
Most importantly: don't try to hide that you're using AI. The goal isn't to trick anyone – it's to use AI as a tool to create genuinely valuable content that serves your audience better than manual processes could at scale.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing AI content:
Focus on use case pages and integration guides where AI can scale technical expertise
Build knowledge bases from customer support conversations and product documentation
Use AI to create personalized onboarding content and help documentation
For your Ecommerce store
For e-commerce stores scaling with AI:
Start with product descriptions and category pages where consistency matters most
Use AI to create buying guides and comparison content that requires industry knowledge
Implement automated content for seasonal campaigns and product launches