Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
While everyone rushed to ChatGPT in late 2022, I made a deliberate choice that probably seemed crazy at the time: I completely avoided AI for two full years. 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.
Here's the thing - when everyone in your industry is screaming about the next revolutionary tool, that's usually the worst time to make strategic decisions about it. I wanted to see what AI actually was, not what VCs and Twitter gurus claimed it would become.
Six months ago, I finally dove in with a scientist's approach rather than a fanboy's enthusiasm. What I discovered challenges everything the AI evangelists have been preaching. The reality is both more boring and more valuable than the hype suggests.
In this playbook, you'll learn:
Why waiting through hype cycles gives you a massive advantage
The specific AI experiments I ran to separate signal from noise
What AI actually excels at (and what it completely fails at)
My framework for evaluating any hyped technology
How to implement AI strategically without getting caught in the bubble
This isn't another "AI will change everything" piece. It's a realistic assessment from someone who deliberately avoided the noise to find the actual signal.
Reality Check
What the AI evangelists won't tell you
The AI industry has been pushing the same narrative since late 2022: AI will revolutionize everything, and if you're not implementing it immediately, you'll be left behind. Every conference, every LinkedIn post, every startup pitch deck follows the same script.
Here's what the conventional wisdom tells you:
AI is "intelligence" that can replace human thinking - The marketing suggests these systems actually understand and reason like humans
You must adopt AI now or become irrelevant - Fear-based messaging that pushes immediate implementation
AI will solve all your business problems - From customer service to content creation to strategic planning
The technology is mature and ready for enterprise - Downplaying the limitations and ongoing development
ROI is immediate and measurable - Promising quick returns without discussing implementation costs
This narrative exists because there's massive money behind it. VCs have invested billions and need exits. Software companies need new product categories to drive growth. Consultants need new services to sell.
But here's where conventional wisdom falls apart: it treats AI like a magic solution rather than what it actually is - a powerful but limited tool with specific use cases. The hype cycle creates unrealistic expectations that lead to failed implementations and wasted resources.
Most businesses following this advice end up with expensive AI tools that don't deliver promised results, frustrated teams, and the conclusion that they "did AI wrong" rather than recognizing the limitations of the technology itself.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
While everyone was rushing to implement ChatGPT in their workflows, I took a completely different approach. I'd seen this movie before with social media, mobile, and cloud computing. The pattern is always the same: massive hype, inflated expectations, reality check, then actual useful applications emerge.
My client roster includes several startups that were pressuring me to "add AI" to everything we were doing. E-commerce sites wanted AI-powered everything. SaaS companies wanted AI chatbots. Everyone wanted to be "AI-first" without understanding what that even meant.
Instead of jumping on the bandwagon, I made a deliberate decision: wait. While competitors were adding "AI-powered" to their service offerings, I focused on what actually worked. This decision cost me some potential clients who wanted the shiny new thing, but it saved me from becoming another failed AI implementation story.
The turning point came when I started seeing the real results from early adopters. Companies that had rushed into AI implementations were quietly rolling them back. Customer service chatbots were creating more problems than they solved. AI-generated content was getting penalized by Google. The promised productivity gains weren't materializing.
That's when I realized the opportunity wasn't in riding the hype - it was in understanding what remained after the hype settled. Six months ago, I started my own systematic exploration of AI, not as a revolutionary technology, but as a tool to be evaluated like any other business investment.
Here's my playbook
What I ended up doing and the results.
My approach to AI evaluation was deliberately methodical. Instead of believing the marketing claims, I designed specific experiments to test what AI could actually do for real business problems.
Test 1: Content Generation at Scale
I generated 20,000 SEO articles across 4 languages for a client's blog. The insight? AI excels at bulk content creation when you provide clear templates and examples. But each article needed a human-crafted example first. AI is a pattern machine, not intelligence.
Test 2: SEO Pattern Analysis
I fed my entire site's performance data to AI to identify which page types convert best. AI spotted patterns in my SEO strategy I'd missed after months of manual analysis. But it 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. AI works excellently for repetitive, text-based administrative tasks. Anything requiring visual creativity or truly novel thinking still needs human input.
The Pattern That Emerged
After six months of testing, a clear picture emerged. AI isn't intelligence - it's a powerful pattern recognition and text manipulation engine. The real equation is simple: Computing Power = Labor Force.
Most people use AI like a magic 8-ball, asking random questions. But the breakthrough comes when you realize AI's true value: it's digital labor that can DO tasks at scale, not just answer questions.
Here's my current framework for any AI implementation:
Identify the 20% of AI capabilities that deliver 80% of value for your specific business
Test with small, measurable experiments before scaling
Keep strategy and creativity firmly in human hands
Use AI as a scaling engine, not a replacement for thinking
Pattern Recognition
AI is incredibly powerful at recognizing patterns in large datasets, but it can't create original strategies or understand context like humans claim it can.
Scale vs Quality
AI works best for bulk tasks where consistency matters more than perfection. One human example can generate thousands of variations.
Digital Labor
The most valuable realization: AI isn't artificial intelligence, it's digital labor. Treat it like hiring a very fast, very literal assistant.
Implementation Reality
Most AI failures come from expecting magic instead of treating it as a tool that requires proper setup, training examples, and realistic expectations.
After six months of systematic testing, the results challenge both the AI evangelists and the skeptics. AI isn't revolutionary, but it's not useless either.
What actually works:
Content automation at scale - Generated 20,000 articles efficiently
Pattern analysis - Identified SEO trends I'd missed manually
Administrative task automation - Eliminated hours of repetitive work
Translation and text manipulation - Handles multilingual content efficiently
What doesn't work:
Visual design beyond basic generation
Strategic thinking and creative problem-solving
Industry-specific insights without training
The ROI is real, but it's in efficiency gains for specific tasks, not revolutionary business transformation. AI saves time on things you're already doing, it doesn't fundamentally change what you should be doing.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from waiting through the hype cycle and testing systematically:
Hype cycles create better opportunities for contrarians - While everyone chases the shiny object, real value emerges in practical applications
AI won't replace you short-term, but it will replace those who refuse to use it strategically - The key is implementation, not adoption
Test before you invest - Small experiments reveal capabilities better than vendor demos
Focus on augmentation, not replacement - AI works best when it enhances human capabilities
Pattern recognition ≠ intelligence - Understanding this limitation prevents unrealistic expectations
The real winners will be those who find specific, valuable use cases - Not those who add "AI" to everything
Distribution still beats technology - AI tools are worthless without proper implementation and training
My operating principle for 2025: Identify the 20% of AI capabilities that deliver 80% of the value for your specific business, then implement those systematically while keeping strategy and creativity in human hands.
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 strategically:
Start with content automation - Use AI for scaling blog posts, documentation, and support materials
Automate customer onboarding sequences - AI excels at personalized email workflows
Use AI for data analysis - Pattern recognition in user behavior and feature usage
For your Ecommerce store
For e-commerce businesses implementing AI:
Product description generation at scale - Perfect for large catalogs across multiple languages
Customer service automation - Handle basic inquiries while escalating complex issues
Inventory forecasting - Use AI for pattern recognition in sales data