Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, a potential client asked me where they could download an AI strategy template. My honest answer? "Don't." Not because templates are bad, but because I've spent 6 months building 15+ AI workflows from scratch, and here's what I learned: the best AI strategy isn't downloaded—it's discovered through experimentation.
After deliberately avoiding AI for two years (yes, while everyone was going crazy over ChatGPT), I finally dove deep. The result? I built AI workflows that generate 20,000+ SEO articles across 4 languages, automate entire client project workflows, and handle everything from content creation to sales pipeline management.
But here's the thing—none of this would have happened if I'd started with someone else's template. Instead of giving you another generic AI strategy document, I'm sharing the actual framework I use with clients, built from real experiments and failures.
Here's what you'll learn:
Why most AI strategy templates set you up for failure
My 3-layer framework for AI implementation that actually works
The specific workflows I've built and their ROI
How to identify your first AI use case without a template
The real costs and timeline of AI adoption (spoiler: it's not what you think)
Ready to build an AI strategy that's tailored to your actual business instead of following someone else's playbook? Let's dive into what actually works.
The Reality
What the AI consulting industry won't tell you
Walk into any AI consulting firm or download any "comprehensive AI strategy template" and you'll see the same pattern. They all follow the classic enterprise consulting approach:
Step 1: Assess your current digital maturity
Step 2: Identify use cases across departments
Step 3: Prioritize based on ROI and complexity
Step 4: Create a 12-month implementation roadmap
Step 5: Establish governance and ethics frameworks
Sounds logical, right? The problem is this approach treats AI like any other technology implementation—ERP systems, CRM migrations, or digital transformations. But AI is fundamentally different.
Here's why these templates fail in practice: AI is a pattern machine, not a strategy machine. You can't strategize your way into understanding what AI can do for your specific business. You have to experiment your way there.
The consulting industry loves these templates because they're billable. A 6-month "AI assessment and strategy development" project sounds impressive. But by the time you've finished analyzing and planning, the AI landscape has shifted, your competitors are already shipping, and you're still stuck in analysis paralysis.
Most importantly, these generic templates miss the fundamental truth about AI adoption: the constraint isn't technology anymore—it's knowing what to build and for whom. Every business has unique processes, data, and challenges that no template can anticipate.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started my AI journey 6 months ago, I made the same mistake everyone makes—I tried to create the perfect strategy upfront. I spent weeks researching frameworks, reading whitepapers, and trying to map out a comprehensive AI adoption plan.
The result? Complete paralysis. Every day, new AI tools launched. Every week, the landscape shifted. My perfect strategy was outdated before I'd even started implementing it.
The breakthrough came when I shifted from "strategy first" to "experiment first." Instead of trying to predict how AI would impact my freelance business, I picked one specific pain point and built a solution.
The pain point? Content creation at scale. I had clients who needed hundreds of SEO-optimized product pages, but manually creating them was impossible. My options were hiring expensive copywriters (who lacked industry knowledge) or training clients to write themselves (which never worked—they didn't have time).
So I built my first AI workflow: automated content generation for a Shopify client with 3,000+ products across 8 languages. No grand strategy, no 12-month roadmap—just solving one specific problem.
What I discovered changed everything. The real value wasn't in the technology itself, but in understanding exactly where AI could replace my manual work while maintaining quality. This led to building 15+ different workflows over 6 months, each solving a specific client challenge.
The pattern became clear: AI works best when you treat it as digital labor, not as intelligence. Stop asking "How can AI make us smarter?" Start asking "What repetitive work can AI do for us?"
Here's my playbook
What I ended up doing and the results.
Based on 6 months of hands-on AI implementation across multiple client projects, here's the framework I actually use—not the one I planned to use.
The 3-Layer AI Implementation Framework:
Layer 1: Pick One Pain Point
Forget about comprehensive AI strategy. Pick the most expensive manual task you do repeatedly. For me, it was content creation. For others, it might be data entry, customer support, or report generation. The key is specificity—"improve marketing" isn't a pain point, "write 500 product descriptions" is.
Layer 2: Build the Minimum Viable Workflow
Create the simplest possible AI solution for that one pain point. I started with a basic prompt that could generate product descriptions. It wasn't perfect, but it worked. Most people try to build the perfect solution immediately—that's backwards. Build something that works, then improve it.
Layer 3: Scale and Systematize
Once you have one working workflow, you understand how AI actually behaves with your data and processes. Now you can expand. I went from product descriptions to full SEO content generation, then to client workflow automation, then to sales pipeline management.
My Specific Implementation Examples:
For the Shopify client, I built a 3-layer content system: knowledge base (industry-specific information), brand voice framework, and SEO architecture integration. The result was 20,000+ pages generated across 8 languages, taking the client from 300 to 5,000+ monthly visitors in 3 months.
For workflow automation, I integrated AI with project management tools to automatically update client documents, maintain project timelines, and generate status reports. This saved 10+ hours per week of administrative work.
For sales automation, I created AI-powered email sequences that adapt based on prospect behavior and industry. The key wasn't replacing human insight—it was automating the repetitive research and drafting work.
The Real Secret: Each workflow required a human-created example first. AI excels at pattern recognition and replication, but you need to show it what "good" looks like in your specific context. The template isn't the strategy—the examples you create are.
This approach works because it's grounded in actual business problems, not theoretical possibilities. You learn by doing, not by planning.
Pain Point Focus
Start with your most expensive manual task. Strategy documents won't help if you don't know what specific problem you're solving.
Minimum Viable Workflow
Build the simplest solution first. Perfect workflows come from iteration not planning. Most fail because they try to solve everything at once.
Human Examples Required
AI needs to see what good looks like in your context. Create one perfect example manually before automating the process.
Pattern Recognition
AI excels at replicating patterns not creating strategy. Focus on tasks with clear inputs and outputs rather than creative decisions.
The real results from this approach have been transformative, not just in metrics but in how I think about business operations.
Quantifiable Outcomes:
The content generation workflow alone produced over 20,000 indexed pages across multiple client projects. One e-commerce client went from under 500 monthly visitors to over 5,000 in three months. But the bigger win was time—what used to take weeks of back-and-forth with copywriters now happens in hours.
Client workflow automation saved an average of 10 hours per week on administrative tasks. Instead of manually updating project documents and sending status reports, everything happens automatically based on project triggers.
Unexpected Discoveries:
The biggest surprise was that AI works best for tasks I thought required "creativity"—like content generation—and struggles with tasks I thought were "simple"—like reliable data formatting. This completely reversed my expectations about where to apply AI first.
Another revelation: the most valuable workflows weren't the most sophisticated ones. Simple automation that runs reliably beats complex AI that needs constant babysitting. The content generation system works because it follows clear patterns, not because it's "smart."
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After 6 months of real-world AI implementation, here are the lessons that actually matter:
1. Strategy Documents Are Procrastination
Every hour spent "strategizing" AI adoption is an hour not spent learning how AI actually works with your data and processes. Start building, not planning.
2. AI Needs Examples, Not Instructions
You can't prompt your way to good results. You need to show AI exactly what you want by creating perfect examples first. The prompt is less important than the training data.
3. Distribution Still Beats Features
Having AI workflows means nothing if they don't solve real customer problems. I've seen businesses build impressive AI features that nobody uses because they didn't focus on distribution first.
4. Budget for API Costs
Most businesses underestimate ongoing AI costs. What seems cheap per request adds up quickly at scale. Factor API costs into your business model from day one.
5. Human Expertise Becomes More Valuable
AI commoditizes the execution, which makes strategy and positioning more important than ever. Your competitive advantage isn't in the AI tools—it's in knowing how to use them for your specific market.
6. Start Where You Have the Most Pain
Don't start where AI is "coolest." Start where manual work is most expensive or time-consuming. The ROI will be obvious and immediate.
7. Templates Can't Predict Your Context
Every business has unique processes, data quality issues, and constraints. No downloaded template can account for your specific situation. Build your strategy through experimentation, not documentation.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to implement this approach:
Start with customer support automation or content generation
Use AI for onboarding email sequences and user engagement
Automate product documentation and help articles
Focus on reducing manual work in your customer success processes
For your Ecommerce store
For e-commerce stores ready to leverage AI workflows:
Begin with product description generation and SEO content
Automate customer review responses and FAQ generation
Use AI for inventory management and demand forecasting
Implement personalized email marketing automation