Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
I remember getting a call from a client last year who'd spent $50,000 on an AI project that was supposed to "revolutionize" their customer service. Six months later, their support team was still manually answering emails, and the AI chatbot was giving customers recipes when they asked about billing issues.
Sound familiar? Here's the uncomfortable truth: most AI implementations fail not because the technology doesn't work, but because businesses fundamentally misunderstand what they're building.
After deliberately avoiding the AI hype for two years and then spending six months testing everything from content generation to sales automation, I've learned that the biggest barrier to AI implementation isn't technical—it's strategic. Companies treat AI like a magic solution when it's actually a very powerful, very dumb tool that needs specific direction.
In this playbook, you'll discover:
Why "AI will replace you" thinking sabotages implementation
The real reason 80% of AI projects fail (hint: it's not the tech)
My 6-month testing framework that separates AI hype from business value
When to automate vs when to augment (most get this backwards)
The hidden costs that kill AI ROI before you even start
If you're tired of AI consultants promising the moon while your actual business problems remain unsolved, this is for you. Let's cut through the hype and talk about what actually works.
Conventional Wisdom
What every startup founder gets pitched about AI
Walk into any tech conference or scroll through LinkedIn, and you'll hear the same AI mantras repeated like gospel:
"AI will 10x your productivity." "AI will replace half your workforce." "If you're not using AI, you're already behind."
The conventional wisdom pushes businesses toward AI like it's a race. Vendors sell AI as a plug-and-play solution that will magically solve your operational challenges. The typical pitch goes something like this:
Identify repetitive tasks that humans currently do
Replace them with AI to cut costs and increase speed
Scale infinitely without hiring more people
Measure success by automation percentage and cost reduction
Integrate everything into one unified AI system
This approach exists because it's easy to sell and sounds transformational. Software vendors make money by convincing you that AI complexity is their problem, not yours. Consultants charge premium rates for "AI transformation" strategies that treat your business like every other business.
But here's where conventional wisdom falls apart: it treats AI like a magic wand instead of a tool that needs specific, expert direction. Most businesses following this playbook end up with expensive automation that doesn't actually solve their core problems. They automate the wrong things, ignore the human elements that make their business unique, and wonder why their AI investment feels like throwing money into a black hole.
The real challenge isn't implementing AI—it's knowing what to implement and why.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
While everyone was rushing into ChatGPT in late 2022, I made a counterintuitive choice: I deliberately avoided AI for two years. Not because I'm a luddite, but because I've seen enough tech hype cycles 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.
When I finally dove in six months ago, I approached it like a scientist, not a fanboy. I had multiple client projects running—B2B SaaS companies needing content at scale, e-commerce stores drowning in product descriptions, agencies burning out their teams on repetitive tasks.
My first attempt was what everyone tries: throwing AI at content creation. I used ChatGPT to write blog posts for a SaaS client. The content was... fine. Generic, but fine. The client published it, got some traffic, but nothing moved the needle. That's when I realized the problem.
I was treating AI like a magic 8-ball, asking random questions and hoping for business transformation.
The breakthrough came when I stopped thinking about AI as "intelligence" and started treating it as what it really is: a pattern machine that can DO tasks at scale, not just answer questions. The equation became clear: Computing Power = Labor Force.
This mindset shift changed everything. Instead of asking "Can AI write my blog?" I started asking "What specific, repetitive task can AI execute consistently if I give it the right framework?"
That's when I began testing AI not as a replacement for human expertise, but as an amplification system for work I was already doing manually.
Here's my playbook
What I ended up doing and the results.
Instead of following the "implement everything" approach, I built a systematic testing framework to identify where AI actually delivers value versus where it's just expensive automation.
Test 1: Content Generation at Scale
I generated 20,000 SEO articles across 4 languages for a client's blog. But here's the key: each article needed a human-crafted example first. AI excels at bulk content creation when you provide clear templates and examples, but it can't create the strategy—only execute it.
The insight: AI works best for repetitive, text-based tasks when you've already proven the format manually.
Test 2: SEO Pattern Analysis
I fed AI my entire site's performance data to identify which page types convert. AI spotted patterns in my SEO strategy I'd missed after months of manual analysis. It couldn't create the strategy, but it could analyze what already existed faster than any human.
The limitation: AI can optimize existing processes but can't invent new approaches.
Test 3: Client Workflow Automation
I built AI systems to update project documents and maintain client workflows. This saved hours of administrative work weekly. AI handled text manipulation, data organization, and maintaining consistency across repetitive tasks.
The discovery: AI's true power isn't in replacing creativity—it's in handling the mundane work that drains creative energy.
My operating principle became: 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 becoming an "AI expert"—it's identifying the 20% of AI capabilities that deliver 80% of the value for your specific business.
For me, that meant using AI as a scaling engine for content and analysis, while keeping strategy and creativity firmly in human hands.
Pattern Recognition
AI excels at recognizing and replicating patterns in your existing successful work, not creating new strategies from scratch.
Time Investment
Expect 3-6 months of testing to find your specific AI sweet spots. Most businesses give up after 2-3 failed experiments.
Quality Control
Every AI output needs human review. Build approval processes early, or you'll spend more time fixing mistakes than you saved.
Hidden Costs
Factor in API costs, training time, and workflow maintenance. Many "cheap" AI solutions become expensive once you scale.
After six months of systematic testing, the results were clear but nuanced:
What Actually Worked:
Content automation saved 15-20 hours per week on repetitive writing tasks
Data analysis uncovered optimization opportunities I'd missed manually
Administrative automation freed up creative time for higher-value work
What Disappointed:
AI couldn't replace strategic thinking or industry-specific insights
Visual generation remained inconsistent for professional use
One-prompt solutions rarely delivered business-quality output
The biggest surprise? The companies that succeeded with AI weren't the ones with the biggest budgets—they were the ones with the clearest understanding of their existing processes.
AI amplified what they were already doing well, rather than trying to create entirely new capabilities.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from my 6-month AI implementation journey:
Start with problems, not technology. Identify specific pain points before looking for AI solutions.
AI is a scaling tool, not a strategy tool. Use it to amplify proven processes, not to create new ones.
One prompt ≠ business solution. Effective AI requires systematic workflows and quality control.
Text manipulation is AI's superpower. Focus on writing, editing, and data organization tasks first.
Budget for ongoing costs. API fees, training time, and maintenance add up quickly.
Human expertise becomes more valuable, not less. AI needs direction from people who understand the domain.
Start small and prove value. Test with low-risk projects before scaling organization-wide.
The companies winning with AI aren't the ones implementing everything—they're the ones implementing the right things strategically.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI:
Focus on customer support automation and content generation first
Use AI for user onboarding sequences and product documentation
Automate sales pipeline tasks but keep strategy human-driven
For your Ecommerce store
For ecommerce stores exploring AI:
Start with product description generation and SEO content creation
Implement AI-powered customer service for common inquiries
Use AI for email marketing personalization and abandoned cart recovery