Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
While everyone rushed to ChatGPT in late 2022, I made a counterintuitive choice: I deliberately avoided AI for two years. Not because I was against technology, but because I've seen enough tech hype cycles to know that the best insights come after the dust settles.
The problem most small businesses face with AI isn't technical - it's knowing what AI actually is versus what Silicon Valley marketing claims it can do. I spent six months testing AI tools across different business functions, and what I discovered will save you from the expensive mistakes most companies are making.
Here's what you'll learn from my real-world AI experiments:
Why AI is a pattern machine, not intelligence (and why this matters)
The one equation that changed how I think about AI: Computing Power = Labor Force
Which 20% of AI capabilities deliver 80% of the value for small businesses
My three-test framework for evaluating AI tools
Real examples from scaling content creation to 20,000 articles across 4 languages
This isn't another "AI will change everything" article. It's a practical guide based on actual experiments with real results and failures.
Reality Check
What the AI industry won't tell you
Every AI vendor wants you to believe their tool is revolutionary. The industry pushes a narrative that AI will replace human intelligence and transform every business overnight. Here's the conventional wisdom you'll hear everywhere:
"AI is intelligent" - Marketing teams call it artificial intelligence to make it sound magical
"One tool solves everything" - Platforms promise to handle all your business needs with a single AI assistant
"Plug and play solutions" - Just sign up and watch AI transform your business automatically
"AI will replace workers" - Fear-based messaging that either your job is doomed or you'll fire half your team
"More features equal better results" - Complex platforms with hundreds of AI capabilities
This conventional approach leads most small businesses down expensive rabbit holes. They either become paralyzed by the hype or waste thousands on tools that don't deliver because they don't understand what AI actually does.
The reality? Most businesses are asking AI to be an assistant when they should be using it as digital labor. They're trying to replace strategic thinking when they should be automating repetitive tasks. The industry sells magic, but what works is much more practical and specific.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When ChatGPT exploded in late 2022, I watched as every consultant, agency, and startup pivoted to become "AI experts" overnight. Instead of jumping on the bandwagon, I made a deliberate choice to wait and observe. I'd seen this pattern before with blockchain, NFTs, and other tech bubbles.
But by early 2024, I couldn't ignore the underlying technology anymore. I was working with multiple clients who needed to scale content creation, and traditional approaches weren't working. One e-commerce client had over 3,000 products that needed descriptions across 8 languages. Another SaaS startup needed to generate hundreds of use-case pages for programmatic SEO.
The manual approach was hitting a wall. Even with dedicated writers, we could maybe produce 10-20 pieces of quality content per month. But to compete in today's market, we needed to scale to hundreds or thousands of pages. That's when I decided to approach AI like a scientist, not a fanboy.
I set up three controlled experiments to test AI's real capabilities versus the marketing hype. Each test focused on a different business function where I had actual experience to compare results. The goal wasn't to find the next shiny object - it was to identify where AI could genuinely solve problems I couldn't solve any other way.
Here's my playbook
What I ended up doing and the results.
Rather than randomly trying AI tools, I designed three specific tests based on real client needs. Each test had clear success metrics and compared AI output to our existing processes.
Test 1: Content Generation at Scale
I needed to generate 20,000 SEO articles across 4 languages for a client's blog. This wasn't about replacing human creativity - it was about scaling a proven content format we'd already validated manually.
The process I built involved three layers: First, I fed AI a comprehensive knowledge base of 200+ industry-specific books and documents from the client's archives. Second, I developed a custom tone-of-voice framework based on their existing brand materials. Third, I created prompts that respected proper SEO structure - internal linking strategies, keyword placement, meta descriptions, and schema markup.
The key insight: AI excels at bulk content creation when you provide clear templates and examples. But here's the limitation - each article type needed a human-crafted example first. AI is a pattern machine, not a creative engine.
Test 2: SEO Pattern Analysis
I fed AI my entire site's performance data to identify which page types convert. Instead of manually analyzing months of data, I let AI spot patterns I'd missed.
The insight: AI identified that my use-case pages with embedded product templates dramatically outperformed traditional feature descriptions. It could analyze thousands of data points and surface correlations I would have taken weeks to find manually.
The limitation: AI couldn't create the strategy - only analyze what already existed. It's a powerful analytical tool, not a strategic thinker.
Test 3: Client Workflow Automation
I built AI systems to update project documents and maintain client workflows. Every time a deal closed in HubSpot, AI would automatically create project folders, generate status reports, and update relevant stakeholders.
What worked: AI handles repetitive, text-based administrative tasks flawlessly. It never forgets steps, maintains consistency, and works 24/7.
What still requires humans: Anything requiring visual creativity, nuanced judgment calls, or truly novel thinking.
Pattern Recognition
AI excels at recognizing and replicating patterns, but calling it "intelligence" is marketing fluff. This distinction defines what you can realistically expect.
Digital Labor
The breakthrough equation: Computing Power = Labor Force. AI doesn't think - it does tasks at scale that would otherwise require human time.
20/80 Rule
Focus on the 20% of AI capabilities that deliver 80% of the value for your specific business rather than trying to use every feature.
Human + AI
AI works best for repetitive, text-based tasks while humans handle strategy, creativity, and complex decision-making.
The results from my six-month AI experiment were both impressive and humbling. On the content generation front, we successfully created those 20,000 articles across 4 languages, something that would have taken a team of writers over two years to complete manually.
For SEO analysis, AI helped me identify performance patterns in my data that led to a complete restructuring of how I approach programmatic content. The automated client workflows saved approximately 10 hours per week of administrative tasks, allowing me to focus on strategic work.
But here's what the AI evangelists won't tell you: the setup time was significant. Each workflow required weeks of fine-tuning, and the AI tools had a steep learning curve. The content generation, while scalable, still needed human oversight for quality control and brand consistency.
Most importantly, AI didn't replace human expertise - it amplified it. The businesses that succeeded with AI already had strong foundations and clear processes. AI became a multiplier, not a replacement for good strategy.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After six months of real-world AI testing, here are the key lessons that will save you time and money:
Start with problems, not tools - Don't ask "How can I use AI?" Ask "What repetitive tasks are slowing my team down?"
AI amplifies existing systems - If your current processes are broken, AI will just break them faster
Quality over quantity - One well-implemented AI workflow beats ten half-finished experiments
Expect a learning curve - Budget 2-3 months to properly implement and optimize any AI solution
Human expertise remains critical - AI is most effective when guided by people who understand the domain
Focus on text and data - AI excels at language, code, and pattern recognition but struggles with visual creativity
Prepare for ongoing costs - AI API costs can add up quickly, especially for high-volume use cases
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 for programmatic SEO and user onboarding
Use AI for customer support ticket routing and initial responses
Automate user behavior analysis and engagement scoring
For your Ecommerce store
For e-commerce stores ready to leverage AI:
Focus on product description generation and SEO optimization at scale
Implement AI for inventory forecasting and demand planning
Use AI for personalized email campaigns and abandoned cart recovery