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: I avoided AI for two years. Not because I was against technology, but because I've seen enough hype cycles to know that the best insights come after the dust settles.
Fast forward to six months ago, when I finally decided to approach AI like a scientist, not a fanboy. What I discovered through hands-on testing completely changed how I run my consulting business – and it had nothing to do with what VCs were claiming AI would do.
The breakthrough wasn't using AI as a magic assistant. It was treating it as digital labor that can DO tasks at scale. This realization helped me build an AI governance framework that actually works for real businesses, not just Silicon Valley startups with unlimited budgets.
In this playbook, you'll learn:
Why I deliberately waited 2 years to adopt AI (and why this timing was perfect)
The real difference between AI as "intelligence" vs. AI as a pattern machine
My 3-test framework for evaluating AI implementation in business
How to identify the 20% of AI capabilities that deliver 80% of the value
A practical governance structure that prevents AI implementation chaos
This isn't another "AI will change everything" article. It's a honest look at what actually works when you strip away the hype and focus on real business results.
Reality Check
What the AI evangelists won't tell you
If you've been following the AI conversation for the past two years, you've probably heard the same promises repeated everywhere:
The Standard AI Governance Advice:
"Implement AI across all departments immediately or get left behind"
"Create an AI committee with representatives from every team"
"Establish comprehensive AI ethics guidelines before starting"
"Invest in AI training for all employees"
"Use AI for everything: customer service, content creation, decision-making"
This advice exists because consultants and software vendors make money from complexity. The more overwhelming AI seems, the more likely you are to hire someone to "help" you navigate it. The AI industry has a vested interest in making you believe that without immediate, comprehensive adoption, your business will become obsolete.
But here's what this conventional wisdom gets wrong: most businesses don't need an AI governance framework – they need an AI reality check. The majority of AI use cases being pushed by vendors are solutions looking for problems, not actual business needs being solved efficiently.
The real challenge isn't governing AI across your entire organization. It's figuring out which 2-3 specific use cases actually move the needle for your business, then implementing those exceptionally well. Everything else is just expensive noise that creates the illusion of progress while burning through budget and team bandwidth.
This is why I took a completely different approach to AI governance – one that starts with skepticism rather than enthusiasm.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, I was running a profitable consulting business helping SaaS and e-commerce companies with growth strategy, website optimization, and content systems. I had deliberately avoided the AI rush, watching from the sidelines as companies burned money on AI tools that promised everything and delivered incremental improvements at best.
But I was facing a real problem: scaling content creation for my clients. I was generating solid results with traditional SEO and content strategies, but the manual work required to maintain quality at scale was becoming a bottleneck. Clients wanted more content, faster iteration, and better results – all while maintaining the strategic insight that made the work valuable.
The typical solutions weren't working:
Hiring more writers: Great writers who understood business strategy were expensive and hard to find. Generic content writers produced mediocre results that diluted the brand voice.
Outsourcing to agencies: Lost control over quality and strategic direction. Content felt generic and disconnected from the client's actual business challenges.
Training client teams: Time-intensive and rarely sustainable. Business owners don't have time to become content strategists.
I was skeptical that AI could solve this problem because everything I'd seen was either completely generic output or required so much human editing that it wasn't actually saving time. But my clients were asking about AI implementation, and I realized I couldn't advise them on something I hadn't tested thoroughly myself.
So I designed a systematic approach to test AI's actual business value, not its theoretical potential. Instead of implementing AI everywhere at once, I treated it like any other business tool: identify specific problems, test solutions, measure results, scale what works.
The key insight that changed everything: AI isn't intelligence, it's a pattern machine with remarkable capabilities for text manipulation and data analysis. Once I stopped thinking of it as "artificial intelligence" and started treating it as "automated pattern recognition," I could evaluate it properly.
Here's my playbook
What I ended up doing and the results.
My AI governance framework emerged from running three specific tests over six months. Each test taught me something different about where AI actually delivers value versus where it creates expensive busy work.
Test 1: Content Generation at Scale
I built a system 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 proven content frameworks that I knew worked.
The process:
Analyzed my 50 highest-performing articles to identify patterns
Created detailed content templates with clear structure and tone guidelines
Built AI workflows that could fill these templates consistently
Implemented quality control checkpoints at every stage
The breakthrough: AI excelled at bulk content creation when I provided human-crafted examples and clear constraints. But every article needed a human-written example first. AI wasn't replacing strategy – it was scaling execution of proven strategies.
Test 2: SEO Pattern Analysis
I fed AI my entire client portfolio's performance data to identify which page types and content approaches were actually driving conversions.
What AI discovered:
Patterns in my SEO strategy I'd missed after months of manual analysis
Correlation between content structure and conversion rates across different industries
Hidden opportunities in keyword gaps that manual research had overlooked
The limitation: AI could spot patterns and analyze existing data brilliantly, but it couldn't create the strategy. It was a powerful analytical tool, not a strategic replacement.
Test 3: Client Workflow Automation
I implemented AI systems to handle project documentation, client communications, and workflow maintenance across my consulting practice.
The results were transformative for repetitive, text-based administrative tasks:
Automated project status updates and client reporting
Standardized documentation across all client projects
Streamlined proposal creation and contract management
But AI completely failed at anything requiring visual creativity, nuanced client relationship management, or truly novel strategic thinking.
My Operating Principle for 2025:
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.
Core Philosophy
AI is computing power = labor force, not intelligence replacement
Implementation Strategy
Start with 3 specific tests, not company-wide adoption
Value Identification
Focus on text manipulation, pattern recognition, and repetitive tasks
Quality Control
Every AI output needs human-crafted examples and constraints
After six months of systematic testing, the results were clear but not what the AI evangelists promised:
Content Generation: Achieved 10x scale in content production while maintaining quality, but only because I invested significant time upfront creating frameworks and examples. AI amplified my expertise rather than replacing it.
Pattern Analysis: Discovered optimization opportunities that would have taken months of manual analysis to identify. AI compressed weeks of data analysis into hours, but the strategic decisions still required human judgment.
Workflow Automation: Reduced administrative overhead by approximately 60%, freeing up time for high-value client strategy work. The ROI was immediate and measurable.
Failed Experiments: AI-generated visual content was unusable without extensive editing. AI customer service responses lacked the nuance required for complex client relationships. AI strategic recommendations were generic and often missed crucial business context.
The most surprising outcome: implementing AI properly required more strategic thinking, not less. The businesses that succeeded with AI were those that could clearly define what they wanted the technology to accomplish before implementing it.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Building an effective AI governance framework taught me lessons that directly contradict most AI adoption advice:
Start small and specific: Don't implement AI everywhere. Pick 2-3 use cases where the value is obvious and measurable.
AI amplifies existing capabilities: If your processes are broken manually, AI will just break them faster and at scale.
Human examples are essential: AI needs to see what "good" looks like before it can replicate quality consistently.
Focus on constraints, not capabilities: The most successful AI implementations had clear boundaries and quality controls.
Text manipulation is AI's sweet spot: Content creation, data analysis, and administrative tasks show immediate ROI. Creative work and strategic thinking still require humans.
Implementation costs are higher than advertised: Factor in training time, quality control systems, and ongoing maintenance.
Embrace the dark funnel: AI's biggest value often comes from tasks you didn't expect, not the use cases vendors promote.
The framework that emerged isn't about governing AI across your entire organization. It's about identifying specific business problems where AI's strengths (pattern recognition, text manipulation, scale) align with your needs, then implementing those solutions with proper constraints and quality controls.
Most importantly: if you can't clearly articulate what success looks like without AI, adding AI won't solve the underlying problem.
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 governance:
Start with customer support automation and content scaling
Use AI for user onboarding sequence optimization
Implement pattern analysis for user behavior and churn prediction
Focus on trial-to-paid conversion workflow automation
For your Ecommerce store
For ecommerce stores implementing AI governance:
Prioritize product description generation and SEO content scaling
Use AI for customer segmentation and personalized email sequences
Implement inventory forecasting and demand prediction
Focus on conversion optimization through automated testing