Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
"Everyone jumped on ChatGPT in late 2022. I deliberately waited two years.
While my peers were building "AI-powered everything," I was watching the hype cycle play out. I've seen enough tech trends to know that the best insights come after the dust settles. I wanted to see what AI actually was, not what VCs claimed it would be.
After six months of systematic testing across multiple client projects, I can tell you this: AI won't replace your marketing team, but marketers using AI will replace those who don't. The catch? Most people are using AI completely wrong.
Here's what you'll learn from my hands-on experiments:
Why AI isn't marketing intelligence—it's digital labor at scale
The specific workflows where AI delivers 80% of the value
How I generated 20,000+ SEO articles using AI without getting penalized
Where AI fails completely (and what still requires human expertise)
My operating framework for AI in marketing automation
This isn't another "AI will change everything" post. This is a reality check based on actual implementation across SaaS and ecommerce projects.
Reality Check
What the AI marketing experts won't tell you
The marketing industry has gone AI-crazy. Every conference, every LinkedIn post, every "growth guru" is promising AI will revolutionize your marketing automation.
Here's what they typically recommend:
AI assistants for everything: Use ChatGPT for email copy, social posts, ad campaigns, and strategic planning
"Intelligent" automation: Let AI make decisions about when to send emails, which audiences to target, and how to optimize campaigns
Content at scale: Generate hundreds of blog posts, social media updates, and marketing materials with AI
Predictive everything: Use AI to predict customer behavior, lifetime value, and optimal pricing
Personalization engines: Create AI-driven personalized experiences for every user
This conventional wisdom exists because it sounds impressive and sells consulting services. The reality? Most of these approaches treat AI like magic rather than understanding what it actually is: a pattern-recognition machine that excels at specific, repetitive tasks.
The problem with the "AI for everything" approach is that it ignores the fundamental truth: AI needs specific direction to do specific tasks. It's not general intelligence—it's digital labor that requires careful management and clear objectives.
Where conventional wisdom falls short is in expecting AI to replace human strategy and creativity. What it actually does best is amplify human expertise through automation at scale.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, I was skeptical about AI in marketing. I'd seen too many "revolutionary" tools that promised everything and delivered generic garbage. But I knew I couldn't ignore it forever.
My first real test came with a B2C Shopify client who had over 3,000 products and needed SEO content across 8 languages. We're talking about 20,000+ pages that needed optimization. Doing this manually would have taken years and cost more than their annual revenue.
I started with the obvious approach—throwing prompts at ChatGPT and hoping for the best. The results were exactly what you'd expect: generic, robotic content that would never rank on Google or convert visitors.
The breakthrough came when I stopped thinking of AI as an assistant and started treating it as a digital employee that needed training. Just like you wouldn't hire someone and expect them to do great work without context, AI needs specific knowledge and clear processes.
For this client, I had to solve three problems:
Knowledge gap: AI didn't understand their industry specifics
Brand voice: Everything sounded like a robot wrote it
Scale requirements: We needed consistency across thousands of pages
The conventional approach of "prompt engineering" wasn't going to cut it. I needed to build a system that could combine AI's processing power with actual business knowledge and brand understanding.
This led me to develop what I now call the "AI knowledge base approach"—essentially training AI models on company-specific information before letting them loose on marketing tasks.
Here's my playbook
What I ended up doing and the results.
Here's the exact system I built for scaling AI marketing automation without losing quality or getting penalized by Google:
Step 1: Knowledge Base Construction
I spent two weeks with the client digging through their archives—industry-specific books, competitor analysis, customer feedback, and product documentation. This became our AI's "training material." You can't expect AI to write good content about your industry if it only knows generic information.
Step 2: Brand Voice Framework
I created a custom tone-of-voice framework based on their existing brand materials and customer communications. This wasn't just "be friendly"—it was specific phrases, sentence structures, and ways of explaining concepts that matched how they actually talked to customers.
Step 3: SEO Architecture Integration
Each piece of content needed proper SEO structure—internal linking strategies, keyword placement, meta descriptions, and schema markup. I built prompts that respected these requirements while maintaining natural language flow.
Step 4: Automation Workflow
Once the system was proven, I automated the entire workflow. Product data went in, optimized content came out, and it uploaded directly to Shopify through their API. But here's the key: automation came after validation, not before.
For my B2B SaaS clients, I adapted this approach for different use cases:
Email sequences: AI generated personalized drip campaigns based on user behavior and industry
Content calendars: Automated blog topic generation aligned with product roadmap and market trends
Lead scoring: AI analyzed prospect behavior to identify sales-ready leads
Customer segmentation: Automated grouping based on usage patterns and business characteristics
The most important lesson: AI works best when it amplifies human expertise, not replaces it. Every successful implementation started with deep human knowledge that AI could then scale.
My operating principle became: identify the 20% of AI capabilities that deliver 80% of the value for your specific business. Don't try to AI-ify everything—focus on repetitive, text-based tasks where consistency and scale matter more than creativity.
Knowledge Base
Build industry-specific training data before expecting AI to understand your market context
Custom Prompts
Develop brand-specific frameworks that go beyond generic "be friendly" instructions
Validation First
Test AI output quality before building automation systems around unproven processes
Strategic Focus
Identify high-impact, repetitive tasks rather than trying to automate everything at once
The results varied by implementation, but the pattern was consistent: AI excelled at scaling proven processes, not creating new strategies.
For the ecommerce client, we achieved a 10x increase in organic traffic within three months—from 300 to over 5,000 monthly visitors. More importantly, Google indexed over 20,000 pages without penalizing us for AI content.
For SaaS clients, the wins were more subtle but equally valuable:
Email open rates improved by 35% through AI-powered subject line optimization
Lead qualification accuracy increased by 60% with automated scoring
Content production speed increased 5x while maintaining quality
The unexpected outcome? AI didn't reduce human workload—it shifted it from execution to strategy. Teams spent less time writing emails and more time analyzing performance and planning campaigns.
What didn't work: AI-generated creative campaigns, strategic planning, and anything requiring visual design beyond basic generation. The technology simply isn't there yet for complex creative work.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After six months of real-world implementation, here are the lessons that matter:
Start with one workflow: Don't try to AI-ify your entire marketing stack. Pick one repetitive task and perfect it.
Quality over quantity: 100 great AI-generated pieces beat 1,000 mediocre ones every time.
Context is everything: Generic AI outputs are worthless. Industry knowledge and brand understanding make all the difference.
Automation follows validation: Prove the process works manually before building automated systems around it.
AI costs add up: API costs, prompt engineering time, and workflow maintenance aren't free. Budget accordingly.
Human oversight remains essential: AI can scale execution, but strategy and creative direction still require human expertise.
The best applications are invisible: The most successful AI implementations feel natural, not robotic.
When this approach works best: High-volume, text-based marketing tasks where consistency matters more than creativity. When it doesn't: Strategic planning, visual creative work, and anything requiring genuine innovation.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI marketing automation:
Start with email sequence automation using customer behavioral data
Build knowledge bases specific to your industry and product
Focus on lead scoring and customer segmentation before content generation
Use AI for A/B testing email subject lines and CTAs
For your Ecommerce store
For ecommerce stores leveraging AI in marketing workflows:
Automate product description generation with brand voice training
Implement AI-powered customer segmentation for email campaigns
Use AI for seasonal content planning and inventory-based messaging
Focus on personalized product recommendations over generic automation