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 a luddite, but because I've seen enough tech hype cycles to know that the best insights come after the dust settles.
Six months ago, I finally dove in. What I discovered wasn't another miracle productivity tool—it was a fundamental shift in how I approach business automation. After testing AI across multiple ecommerce projects and content generation workflows, I've developed a framework that cuts through the hype.
Most businesses treat AI like a magic 8-ball, asking random questions and hoping for miracles. That's backwards. The real value comes from treating AI as digital labor that can DO tasks at scale, not just answer them.
Here's what you'll learn from my 6-month experiment:
Why waiting to adopt AI was actually the right strategic move
The three-phase framework I used to test AI capabilities systematically
Real examples of where AI delivered massive value (and where it completely failed)
How to identify the 20% of AI capabilities that deliver 80% of the value for your business
A practical roadmap for implementing AI workflows without getting caught in the hype
Reality Check
What the AI evangelists won't tell you
Every AI consultant and SaaS tool wants you to believe that AI will revolutionize your business overnight. The messaging is consistent across the industry:
"AI will replace human workers" - Companies are sold on the idea that AI can immediately substitute human intelligence across most knowledge work
"Implement AI everywhere" - The advice is to integrate AI into every possible business process simultaneously
"More AI tools = more productivity" - The solution to any business problem is adding another AI-powered software to your stack
"AI will think for you" - Most people expect AI to provide strategic insights and make complex decisions
"Start immediately or fall behind" - The fear-based messaging that delayed adoption means competitive disadvantage
This conventional wisdom exists because there's massive money in selling AI solutions. VCs have poured billions into AI startups, and everyone needs to justify those valuations. The result? A market full of solutions looking for problems.
Here's where this advice falls short: AI is not intelligence—it's a pattern machine. It excels at recognizing and replicating patterns, but calling it "intelligence" is marketing fluff. Most businesses that follow the "AI everywhere" approach end up with expensive tools that deliver minimal value because they're treating AI like a strategic consultant instead of digital labor.
The real question isn't "How can AI think for me?" It's "What repetitive, pattern-based work can AI do at scale?" That mindset shift changes everything.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I deliberately avoided AI for two years while everyone else jumped on the hype train. My reasoning was simple: I've seen enough tech bubbles (remember the metaverse?) to know that the best insights come after the initial excitement fades.
When I finally decided to experiment with AI six months ago, I was working with several clients who were struggling with the same fundamental problem: scaling content creation and business processes without burning through their budgets on expensive tools or team expansions.
One client was a B2C Shopify store with over 3,000 products across 8 languages. They needed SEO-optimized content at massive scale, but hiring writers for 20,000+ pages would have cost more than their entire marketing budget. Another was a B2B startup that wanted to automate their client onboarding workflows but couldn't afford enterprise-level automation platforms.
My first instinct was to approach AI like most consultants: ask it to solve strategic problems, generate creative ideas, maybe write some blog posts. That approach failed spectacularly. The content was generic, the strategic advice was surface-level, and the creative ideas felt like they came from a committee.
The breakthrough came when I stopped thinking of AI as an assistant and started treating it as a worker. Instead of asking "What should my content strategy be?" I started asking "Can you write 100 product descriptions following this exact template and tone of voice?" Instead of "How should I automate my business?" I asked "Can you process this spreadsheet of customer data and update my CRM according to these specific rules?"
That shift—from AI as consultant to AI as digital labor—unlocked its real potential. But I needed a systematic way to test what AI could actually do versus what it promised to do.
Here's my playbook
What I ended up doing and the results.
After six months of systematic testing, I developed a three-phase framework that helps businesses identify where AI delivers real value versus where it's just expensive noise.
Phase 1: Digital Labor Audit (Month 1)
I started by cataloging every repetitive, pattern-based task in the business. Not strategy or creativity—pure execution work. For my Shopify client, this included:
Writing product descriptions following a specific template
Generating SEO metadata for thousands of pages
Translating content across 8 languages
Categorizing products into the right collections
The key insight: AI excels at tasks that have clear input-output relationships and can be defined by examples. If you can't explain the task with a clear template and examples, AI probably isn't the right solution.
Phase 2: Systematic Testing (Months 2-4)
I ran three specific tests across different business functions:
Test 1: Content Generation at Scale
Instead of asking AI to "write good content," I provided detailed examples of successful product descriptions, tone of voice guidelines, and specific formatting requirements. The result: AI generated 20,000 SEO articles across 4 languages that were actually usable. The secret was giving AI a template to follow, not asking it to be creative.
Test 2: Business Process Automation
I built AI workflows to handle repetitive administrative tasks—updating project documents, maintaining client workflows, processing data from forms into CRM systems. This delivered immediate time savings because it automated work that was already systematized.
Test 3: Data Analysis and Pattern Recognition
Fed AI my website's performance data to identify which page types converted best. It spotted patterns in SEO strategy I'd missed after months of manual analysis. But it couldn't create the strategy—only analyze what already existed.
Phase 3: Implementation at Scale (Months 5-6)
The final phase focused on building sustainable AI workflows that could run without constant supervision. This meant:
Creating detailed prompt libraries - Every successful AI task needed a repeatable prompt with examples
Building quality control systems - AI output needed human review workflows, especially for client-facing content
Integrating with existing tools - The best AI implementations connected with tools we were already using (CRM, project management, content systems)
The framework revealed something crucial: AI doesn't replace strategic thinking—it amplifies execution capacity. When I had 100 product descriptions to write, AI helped me do it in hours instead of weeks. When I needed to analyze patterns in large datasets, AI spotted correlations I would have missed. But it never replaced the need to understand what questions to ask or how to interpret the results.
Pattern Recognition
AI excels at spotting patterns in large datasets that humans miss - like identifying which page types convert best or which content structures perform in SEO
Template Execution
When you provide clear examples and formatting rules, AI can execute repetitive tasks at massive scale - like generating 20,000 product descriptions consistently
Human + AI Hybrid
The most effective approach combines human strategy and AI execution - humans decide what to do, AI handles the repetitive work at scale
Cost vs. Value
Calculate AI ROI by comparing time saved on repetitive tasks vs. subscription costs - content generation and data processing typically deliver fastest payback
The results from this systematic approach were significant, though not in the ways most AI evangelists promise:
Content Generation Impact: The Shopify client went from 300 monthly organic visitors to over 5,000 within three months. This wasn't because AI wrote "better" content—it was because AI enabled us to create content at a scale that would have been impossible manually. We generated 20,000 pages across 8 languages in the time it would have taken to write 200 manually.
Process Automation Savings: Administrative tasks that were taking 10-15 hours per week got reduced to 2-3 hours of AI workflow setup and quality review. The time savings compounded monthly, freeing up capacity for strategic work that actually required human insight.
Pattern Discovery: AI analysis of website performance data revealed that specific page structures were converting 3x better than others. This insight led to restructuring content strategy based on data patterns, not hunches.
But here's what didn't work: AI couldn't replace strategic decision-making, creative problem-solving, or client relationship management. Every attempt to use AI for high-level strategy or creative work resulted in generic output that needed complete human rework.
The real breakthrough was understanding that AI's value comes from scale, not intelligence. It's digital labor that can execute well-defined tasks consistently, not a replacement for human judgment.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Wait for the right timing: Being late to AI adoption was actually strategic - avoiding the initial hype let me focus on practical applications rather than getting caught up in unrealistic expectations
Treat AI as labor, not intelligence: The most successful implementations came from viewing AI as a worker who can execute defined tasks, not as a consultant who can think strategically
Start with repetitive tasks: AI delivers fastest ROI on work that's already systematized and pattern-based - content generation, data processing, administrative workflows
Template-first approach works: Every successful AI implementation required detailed examples and clear formatting rules - the better your templates, the better your results
Quality control is essential: AI output needs human review systems, especially for anything client-facing or business-critical
Integration beats innovation: The best AI tools were ones that worked with existing business systems rather than requiring completely new workflows
Scale is the real value: AI's advantage isn't doing things better than humans - it's doing defined tasks at massive scale without fatigue or inconsistency
If I were starting this framework again, I'd focus even more heavily on identifying tasks that are already systematized. The businesses that struggle with AI adoption are usually the ones trying to use it for work that isn't clearly defined yet. Systematize first, then automate.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this framework:
Start with content generation for SEO and user onboarding materials
Automate customer support ticket categorization and initial responses
Use AI for lead scoring and CRM data processing
Focus on reducing time-to-market for repetitive product documentation
For your Ecommerce store
For ecommerce stores implementing this framework:
Prioritize product description generation and SEO metadata at scale
Automate customer email sequences and abandoned cart recovery
Use AI for inventory forecasting and trend analysis
Focus on multilingual content creation for international expansion