Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
When everyone started rushing to ChatGPT in late 2022, I made a deliberate choice that probably seemed crazy at the time: I avoided AI for two years. Not because I was anti-technology, but because I've seen enough hype cycles to know that the best insights come after the dust settles.
Then, six months ago, I decided it was time. I approached AI like a scientist, not a fanboy. I wanted to see what it actually was, not what VCs claimed it would be. What I discovered fundamentally changed how I think about marketing automation.
Most people are using AI like a magic 8-ball, asking random questions and hoping for miracles. But here's what I learned after implementing AI across multiple client projects and generating 20,000 SEO articles across 4 languages: AI isn't intelligence—it's digital labor that can DO tasks at scale.
In this playbook, you'll learn:
The three categories of marketing tasks AI actually excels at (and the ones it fails miserably)
My real-world experiments with AI automation across content, outreach, and analytics
The counterintuitive equation that changes everything: Computing Power = Labor Force
Specific workflows that scaled my content production 10x without sacrificing quality
Why most AI marketing implementations fail (and how to avoid these expensive mistakes)
Reality Check
What the AI marketing gurus won't tell you
If you've been following the AI marketing space, you've probably heard the same promises repeated everywhere: "AI will revolutionize your marketing!" "Automate everything with ChatGPT!" "Replace your entire marketing team with AI!"
Here's what the industry typically recommends:
Use AI for everything - From strategy to execution, AI should handle it all
ChatGPT as your marketing assistant - Ask it questions and use the responses directly
One-prompt solutions - Magic prompts that solve complex marketing challenges instantly
Replace human creativity - AI can generate better ideas than your team
Set it and forget it automation - Build it once, let it run forever
This conventional wisdom exists because AI companies need to sell dreams, not reality. The marketing around AI tools focuses on the 10% of amazing results while ignoring the 90% of mediocre or failed attempts.
Where this approach falls short in practice is brutal: businesses waste months building elaborate AI systems that produce generic, unhelpful content. They treat AI like magic instead of understanding it as a very powerful but limited tool.
The real challenge isn't technical—it's strategic. Most people don't understand what AI is actually good at, so they use it for everything and get disappointed when it doesn't deliver miracles.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I started working with clients who'd already tried "AI marketing." One SaaS startup had spent three months building an AI content system that produced articles nobody wanted to read. Another e-commerce client had automated their email campaigns with AI, resulting in a 40% drop in engagement rates.
I realized I needed to approach this systematically. While everyone was asking AI random questions, I wanted to understand: what specific marketing tasks can AI actually handle well, and what should remain human-driven?
My first test was with content generation. I had a B2C Shopify client with over 3,000 products who needed SEO content across 8 languages. Manual content creation would have taken years and cost tens of thousands of dollars.
My second experiment focused on pattern recognition. I fed AI my entire site's performance data to identify which page types converted best. This wasn't about generating content—it was about analyzing patterns in data I'd been manually reviewing for months.
The third test was workflow automation. Instead of asking AI to "do marketing," I built specific systems to handle repetitive, text-based administrative tasks like updating project documents and maintaining client workflows.
What I discovered challenges everything the AI marketing industry preaches. AI isn't replacing marketers—it's amplifying the ones who understand how to use it as digital labor, not digital intelligence.
Here's my playbook
What I ended up doing and the results.
Here's the framework I developed after six months of real-world AI implementation across multiple client projects:
The Three-Layer AI Marketing System
Layer 1: Bulk Content Operations
This is where AI truly excels. For my Shopify client, I built an AI workflow that generated 20,000 SEO articles across 4 languages. But here's the key: I didn't just throw prompts at ChatGPT.
I created a systematic approach:
Built a knowledge base with industry-specific information that competitors couldn't replicate
Developed custom tone-of-voice frameworks based on existing brand materials
Created prompts that respected SEO architecture—internal linking, keyword placement, meta descriptions
Automated the workflow to generate, translate, and upload content directly to the platform
The result? We went from 300 monthly visitors to over 5,000 in three months. Not because AI was magic, but because we treated it as scalable labor.
Layer 2: Pattern Recognition and Analysis
AI spotted patterns in my SEO strategy I'd missed after months of manual analysis. Instead of asking "What should my marketing strategy be?" I fed it specific data: "Analyze these 500 pages and identify which content types drive the most conversions."
For a B2B startup website project, I used Perplexity Pro to build an entire keyword strategy in hours instead of days. But I wasn't asking for generic keywords—I was researching specific market contexts and competitive landscapes.
Layer 3: Administrative Automation
This is the unglamorous but incredibly valuable application. I automated:
Translation of content across multiple languages
Updates to specific documents and client project workflows
Content calendar management and scheduling
Basic reporting and data compilation
The Reality Check Framework
Before implementing any AI marketing automation, I now run every task through this filter:
Is it text-based? AI handles language tasks well
Is it repetitive? Perfect for automation
Does it require industry-specific knowledge? You need to provide this context
Can you provide a manual example first? Essential for quality output
Knowledge Base
Build industry-specific databases that AI can reference, not generic prompts that produce generic results.
Computing Power
Treat AI as digital labor force expansion, not artificial intelligence replacement for human strategy.
Pattern Recognition
Use AI to analyze existing data and identify trends you've missed, not to create strategy from scratch.
Manual Examples
Always create the first version manually to train AI on your specific quality standards and voice.
The transformation was dramatic but took time to compound. Within the first month of implementing my three-layer system, I could see immediate improvements in content production speed. What used to take a full day of writing now took 2-3 hours of setup and review.
For content generation, the numbers were staggering: 20,000 articles produced across 4 languages for one client, resulting in a 10x increase in organic traffic. But the real value wasn't just volume—it was consistency at scale.
The pattern recognition experiments revealed insights that manual analysis had missed. AI identified that certain page types were converting 3x better than others, leading to strategic pivots that improved overall website performance.
Perhaps most importantly, the administrative automation freed up 15-20 hours per week that I could redirect toward strategy and client relationships. The mundane tasks that used to drain energy were now handled systematically.
The timeline was crucial: Month 1 focused on setup and testing, Month 2-3 on optimization and scaling, Months 4-6 on refinement and measurement. This wasn't an overnight transformation—it was a systematic rebuilding of how marketing operations work.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven key lessons learned from six months of systematic AI implementation:
AI is a pattern machine, not intelligence. It excels at recognizing and replicating patterns but calling it "intelligence" is marketing fluff.
The real equation is Computing Power = Labor Force. AI doesn't think—it works. Use it to DO tasks at scale.
Generic input produces generic output. The quality of your AI results directly correlates to the quality of your input and context.
Human expertise becomes more valuable, not less. Someone needs to provide the knowledge, context, and quality control.
Visual creativity still requires humans. AI can handle text but struggles with truly novel visual concepts beyond basic generation.
Industry-specific knowledge isn't in training data. You need to provide the context that makes output valuable.
The best AI workflows combine human strategy with machine execution. Let humans decide what to do, let AI figure out how to do it at scale.
What I'd do differently: Start smaller with one specific workflow rather than trying to automate everything at once. Focus on measurable, repetitive tasks before moving to complex content generation.
This approach works best for businesses with clear content needs and established expertise. It doesn't work when you're trying to use AI to figure out what you should be doing strategically.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS implementation:
Start with customer support automation and knowledge base generation
Use AI for user onboarding email sequences and drip campaigns
Automate competitive analysis and market research compilation
Focus on pattern recognition in user behavior data
For your Ecommerce store
For e-commerce implementation:
Generate product descriptions and SEO content at scale
Automate review responses and customer communication
Use AI for inventory forecasting and demand analysis
Implement dynamic email personalization based on purchase history