Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Two years ago, I made a controversial decision: I deliberately avoided AI while everyone else rushed to ChatGPT. Not because I was a luddite, but because I've seen enough tech hype cycles to know that the best insights come after the dust settles.
While VCs were throwing money at anything with "AI" in the name and founders were scrambling to add ChatGPT to their stack, I waited. I wanted to see what AI actually was, not what everyone claimed it would become.
Six months ago, I finally dove in. Not with the fanboy enthusiasm that dominated 2023, but with the methodical approach of someone who's built real businesses. What I discovered changed how I think about AI in startups completely.
Here's what you'll learn from my systematic AI evaluation:
Why I deliberately waited 2 years to explore AI (and why this timing was perfect)
The 3 AI implementation tests that revealed what actually works vs. what's hype
My framework for identifying the 20% of AI capabilities that deliver 80% of the value
Real examples from scaling content from 0 to 20,000 articles across 4 languages
How to approach AI as digital labor, not magic (this changes everything)
This isn't another "AI will revolutionize everything" piece. It's a practical blueprint from someone who approached AI like a scientist, not a fanboy. Check out more AI strategies here.
Reality Check
What every startup founder keeps hearing about AI
If you're a startup founder, you've been bombarded with AI advice for the past two years. The narrative is always the same: "AI will revolutionize your business," "You need AI or you'll be left behind," and "Every company is now an AI company."
The typical startup AI playbook looks like this:
Rush to implement ChatGPT - Add it to your product as quickly as possible
Automate everything - Let AI handle customer service, content creation, and decision-making
Raise funding with AI buzzwords - Pitch your company as "AI-powered" or "AI-native"
Replace human workers - Cut costs by automating jobs
Move fast and break things - Implement first, figure out the strategy later
This conventional wisdom exists because of FOMO. When OpenAI released ChatGPT, it created a gold rush mentality. VCs started asking about AI strategies in every pitch meeting. Competitors claimed to be "AI-first." Nobody wanted to be the company that missed the next big wave.
But here's where this approach falls short: it treats AI as magic instead of labor. Most founders are using AI like a magic 8-ball, asking random questions and hoping for brilliant insights. They're missing the fundamental insight about what AI actually is: a pattern recognition machine that excels at doing tasks, not thinking.
The result? Startups waste months trying to shoehorn AI into places it doesn't belong, while missing the real opportunities where AI could dramatically improve their operations.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My journey with AI started with deliberate skepticism. While everyone was rushing to implement ChatGPT in late 2022, I made a counterintuitive choice: I deliberately avoided AI for two full years.
This wasn't because I was anti-technology. I've been building websites and helping startups grow for over 7 years. I've seen enough hype cycles - remember when everyone needed a blockchain strategy? - to know that the best insights come after the initial frenzy dies down.
I wanted to see what AI actually was, not what VCs claimed it would become.
Six months ago, I approached AI like a scientist, not a fanboy. Instead of asking "How can AI revolutionize my business?" I asked "What specific tasks could AI actually do better than humans?"
My situation was perfect for this experiment. I was running a freelance business helping startups with growth and website optimization. I had real problems that needed solving:
Content creation at scale - Clients needed hundreds of SEO articles but couldn't afford traditional writers
Administrative overhead - Project updates, client communications, and workflow management was eating my time
Analysis paralysis - I had months of SEO and marketing data but struggled to spot patterns quickly
What I tried first was the standard approach. I signed up for ChatGPT Plus and started asking it questions about my business. "How can I improve my client's conversion rates?" "What's the best SEO strategy for SaaS?" "Write me a marketing plan."
The results were mediocre at best. The answers were generic, the advice was surface-level, and I could have found better insights by googling for 10 minutes. This confirmed my suspicion that most people were using AI wrong.
That's when I had my breakthrough insight: AI isn't intelligence, it's digital labor. The value isn't in asking it to think for you - it's in getting it to do specific tasks that would take humans hours to complete.
Here's my playbook
What I ended up doing and the results.
Once I shifted from treating AI as an assistant to treating it as digital labor, everything changed. I designed three specific tests to evaluate AI's real value for startups.
Test 1: Content Generation at Scale
Instead of asking AI to "write good content," I built a system to generate 20,000 SEO articles across 4 languages. Here's the exact process:
First, I created a knowledge base by scanning through 200+ industry-specific books from my client's archives. This became the foundation - real, deep, industry-specific information that competitors couldn't replicate.
Next, I developed a custom tone-of-voice framework based on the client's existing brand materials and customer communications. Every piece of content needed to sound like them, not like a robot.
Finally, I created prompts that respected proper SEO structure - internal linking strategies, backlink opportunities, keyword placement, meta descriptions, and schema markup. Each piece wasn't just written; it was architected.
The key insight: AI excels at bulk content creation when you provide clear templates and examples. But each article needed a human-crafted example first.
Test 2: SEO Pattern Analysis
I fed AI my entire site's performance data to identify which page types convert best. Instead of spending weeks manually analyzing spreadsheets, AI spotted patterns I'd missed after months of manual analysis.
For example, it identified that programmatic pages with embedded product templates had 3x higher engagement than static feature descriptions. This insight led to restructuring my entire content strategy.
The limitation: AI couldn't create the strategy - only analyze what already existed.
Test 3: Client Workflow Automation
I built AI systems to update project documents and maintain client workflows. Instead of spending 2 hours weekly updating status reports, AI could generate them in 5 minutes based on project data.
The sweet spot: AI works best for repetitive, text-based administrative tasks where the input and output formats are consistent.
My Operating Framework
After these tests, I developed a simple framework: AI won't replace you in the short term, but it will replace those who refuse to use it as a tool.
The key isn't to become an "AI expert" - it's to identify the 20% of AI capabilities that deliver 80% of the value for your specific business.
Content Scaling
Generated 20,000 articles using AI with custom knowledge base and brand voice frameworks
Pattern Recognition
Used AI to analyze SEO data and spot conversion patterns that took months to identify manually
Administrative Tasks
Automated project updates and client communications, saving 8+ hours weekly on repetitive work
Strategic Focus
Focused on AI as digital labor for specific tasks rather than general intelligence or decision-making
The results from my systematic approach were significant and measurable:
Content Operations: What previously took 3-4 weeks to produce manually (50-100 articles) now took 2-3 days with AI automation. The quality remained high because of the custom knowledge base and brand voice training.
Time Savings: Administrative tasks that consumed 8-10 hours weekly were reduced to 1-2 hours. This freed up 6-8 hours for actual strategy and client work.
Analysis Speed: SEO pattern recognition that took weeks of manual spreadsheet analysis now happened in hours. This led to faster iteration cycles and better client results.
Unexpected Outcomes: The biggest surprise was how AI improved consistency rather than just speed. When you create proper templates and examples, AI maintains quality standards better than multiple human contractors.
Timeline-wise, the initial setup took about 6 weeks to build the systems properly. But once operational, the ROI was immediate - especially for content-heavy projects.
What didn't work: Trying to use AI for creative strategy or industry-specific insights without proper training data. AI can't replace domain expertise - it can only amplify it.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven key lessons from my 6-month AI implementation:
AI is a pattern machine, not intelligence - It excels at recognizing and replicating patterns, but calling it "intelligence" sets wrong expectations
Computing Power = Labor Force - The breakthrough came when I realized AI's true value: digital labor that can DO tasks at scale
Templates and examples are everything - AI needs specific direction to produce quality output. Generic prompts produce generic results
Domain expertise still required - AI can't create knowledge you don't have. It can only scale and systematize what you already know
Start with repetitive tasks - The highest ROI comes from automating tasks you're already doing manually and consistently
Quality control is critical - Every AI system needs human oversight and quality checkpoints built in
Implementation takes time upfront - Expect 4-6 weeks to build proper systems, but the ROI compounds quickly after that
What I'd do differently: I'd start with smaller, simpler automation tasks before building complex content systems. The learning curve is steep, and small wins build confidence.
When this approach works best: For startups with clear, repeatable processes that involve text manipulation, data analysis, or content creation. When it doesn't work: For creative strategy, industry insights, or anything requiring visual design beyond basic generation.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Start with customer support automation using your existing knowledge base
Use AI for generating help documentation and onboarding content at scale
Implement AI-powered user behavior analysis to identify churn patterns
Automate trial user segmentation and personalized email sequences
For your Ecommerce store
For ecommerce businesses:
Generate product descriptions at scale using AI with brand voice training
Automate customer review analysis to identify product improvement opportunities
Use AI for dynamic pricing analysis based on competitor and demand data
Implement AI-powered inventory forecasting to reduce stockouts