Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Six months ago, I was drowning in client decisions. Every project brought hundreds of small choices: which keywords to target, what features to prioritize, which automation workflows to build first. I was spending more time deciding than actually implementing.
That's when I realized something most business owners miss: decision fatigue isn't just about being tired—it's about inconsistent results. When you're making dozens of strategic choices daily, your judgment gets cloudy, and your business outcomes become unpredictable.
While everyone's chasing AI solutions that promise to "think" for you, I took a different approach. Instead of relying on black-box algorithms that I couldn't control or understand, I built my own automated decision-making engine using simple rules and workflows.
The result? I've eliminated 80% of my daily business decisions while improving the consistency of my outcomes. My client projects now follow repeatable frameworks that deliver predictable results, and I've freed up mental bandwidth for the decisions that actually matter.
Here's what you'll learn:
Why most AI "decision-making" tools fail in real business contexts
How to identify which decisions to automate vs. which to keep manual
My framework for building decision trees that actually work
The specific tools I use to automate 200+ weekly business decisions
How this approach scales from solo founder to team operations
This isn't about replacing human judgment—it's about using AI strategically to eliminate decision overhead so you can focus on what drives growth.
Industry Reality
What everyone thinks automated decision-making means
When most people hear "automated decision-making," they immediately think of AI. Machine learning algorithms that magically understand your business and make perfect choices. Platforms promising to "eliminate human error" with sophisticated neural networks.
The industry has convinced us that decision automation requires complex AI systems. Companies are spending thousands on platforms that claim to:
Predict customer behavior with 95% accuracy using deep learning
Optimize pricing in real-time based on market conditions
Automate content strategy by analyzing competitor performance
Choose marketing channels based on predictive analytics
Manage inventory through demand forecasting algorithms
This conventional wisdom exists because it sounds impressive and sells expensive software licenses. The promise is seductive: upload your data, and the AI will handle everything.
But here's where it falls short in practice: these systems are black boxes that make decisions you can't understand or control. When they're wrong—and they will be wrong—you have no way to fix them or learn from the mistakes.
I've watched startups spend months integrating "intelligent" decision-making platforms, only to discover the AI doesn't understand their specific business context. The algorithms optimize for metrics that don't actually drive revenue, or they make recommendations that ignore crucial business constraints.
The real problem isn't that we need smarter AI—it's that we need more systematic thinking about which decisions actually matter and how to make them consistently.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breaking point came during a client project last year. I was working with a B2B SaaS startup that had implemented an "AI-powered marketing optimization platform." This system was supposed to automatically allocate their ad spend across channels based on conversion data.
For three months, the AI confidently shifted budget between Facebook, Google, and LinkedIn. The dashboard showed impressive metrics—CTR up 23%, CPC down 15%. The founders were thrilled with their "intelligent" marketing engine.
Then we dug into the actual revenue numbers. Despite all the optimization, their customer acquisition cost had increased by 40%. The AI was optimizing for clicks and engagement, not for the quality of leads that actually converted to paying customers.
When we tried to understand why the system made specific decisions, we hit a wall. The platform's "explainable AI" feature gave us generic explanations like "optimizing for engagement signals" but couldn't tell us why it had decided to spend 60% of the budget on LinkedIn for a product that clearly performed better with Google Ads traffic.
That's when I realized the fundamental flaw in AI-driven decision-making: these systems optimize for data patterns, not business outcomes. They can spot correlations in your data, but they can't understand your business strategy, market context, or long-term goals.
I started questioning every automated decision in my own business. Why was I letting algorithms choose which blog topics to write about? Why was I trusting AI to prioritize feature requests? Why was I using black-box tools to make decisions I should understand?
The answer was simple: because I thought automation meant AI, and I thought AI meant better decisions. But what I actually needed was systematic decision-making that I could control, understand, and improve.
Here's my playbook
What I ended up doing and the results.
Instead of relying on AI platforms, I built my own automated decision-making engine using a combination of simple rules, decision trees, and workflow automation. The key insight: most business decisions follow predictable patterns once you make the logic explicit.
Here's the exact framework I developed:
Step 1: Decision Audit
I spent two weeks tracking every business decision I made. Client onboarding choices, project prioritization, content topics, tool selections, pricing adjustments—everything went into a spreadsheet. The pattern was clear: 80% of my decisions were variations of the same 20 core decision types.
Step 2: Decision Classification
I categorized each decision into three buckets:
Automate: Repetitive decisions with clear criteria (which SEO tools to use based on client size)
Template: Complex decisions that benefit from frameworks (how to structure client proposals)
Human: Strategic decisions requiring judgment (whether to take on a new type of client)
Step 3: Rule Engine Construction
For each "automate" decision, I created explicit if-then rules. Not AI algorithms—simple logic that I could understand and modify. For example:
If client budget > $10k AND industry = SaaS → Use premium SEO tool stack
If project type = ecommerce AND product count > 1000 → Implement programmatic SEO approach
If client mentions competitors → Run automated competitive analysis workflow
Step 4: Workflow Implementation
I used Zapier and Make to implement these rules as automated workflows. When specific triggers occurred (new client signs contract, project phase completes, performance metric hits threshold), the system automatically executed the predetermined decision.
The crucial difference from AI platforms: I could see exactly why each decision was made and modify the rules when business conditions changed. No black boxes, no mysterious algorithms—just clear logic that evolved with my experience.
Within three months, I had automated 200+ weekly decisions while maintaining complete control over the decision-making process. More importantly, the consistency of outcomes improved dramatically because emotions and fatigue were no longer influencing critical business choices.
Decision Mapping
I created visual flowcharts for every recurring business decision. This made it clear which choices could be automated versus which required human judgment.
Rule Engine
Simple if-then logic replaced complex AI algorithms. Each rule was transparent and modifiable based on real business results rather than data correlations.
Workflow Integration
Zapier and Make connected the decision rules to actual business processes. When triggers occurred, the right actions happened automatically without human intervention.
Performance Tracking
Every automated decision generated data on outcomes. This feedback loop allowed me to refine rules based on actual results rather than assumptions.
The impact was immediate and measurable. Within the first month, I had eliminated decision fatigue from my daily routine. Client onboarding went from a series of ad-hoc choices to a systematic process that consistently delivered the right approach for each business type.
More importantly, the quality of my decisions improved. By removing emotional and fatigue-based factors, the automated system consistently chose strategies that aligned with proven frameworks rather than whatever felt right in the moment.
The time savings were significant—about 8 hours per week that previously went to decision-making overhead. But the real value was mental clarity. When you're not constantly making small decisions, you have more cognitive bandwidth for the strategic choices that actually drive business growth.
Client outcomes improved across the board. Projects followed more consistent methodologies, deliverables maintained higher standards, and results became more predictable. The systematic approach eliminated the variability that came from mood-dependent decision-making.
Six months later, I've expanded this framework to automate everything from content planning to client communication workflows. The system now handles the operational decisions automatically while alerting me only when strategic input is actually needed.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Building your own decision engine teaches you more about your business than any AI platform ever could. Here are the key lessons from this experiment:
Most decisions are patterns: Once you track your choices systematically, you'll see that 80% follow predictable logic that can be automated
Transparency beats sophistication: Simple rules you understand and control are more valuable than complex algorithms you can't modify
Decision fatigue is real: Eliminating low-value choices dramatically improves your judgment on high-value strategic decisions
Feedback loops are crucial: Your decision engine should generate data that helps you improve the rules over time
Start small and expand: Begin with one decision type and prove the system works before scaling to multiple areas
Human oversight stays essential: Automation should handle routine decisions while escalating strategic choices to you
Context matters more than data: Your business knowledge should drive the rules, not just data patterns from AI analysis
The biggest insight: decision automation isn't about replacing human judgment—it's about systematizing routine choices so your judgment can focus on what actually matters. When you remove decision overhead from daily operations, you create space for the strategic thinking that drives real growth.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, implement this approach by:
Automating feature prioritization based on user tier and feedback volume
Creating rules for trial-to-paid conversion follow-ups
Systematizing customer success workflows based on usage patterns
Automating pricing tier recommendations for new signups
For your Ecommerce store
For ecommerce stores, focus on:
Automating inventory reorder decisions based on sales velocity
Creating rules for discount timing and percentage based on inventory levels
Systematizing product bundling recommendations based on purchase history
Automating supplier selection based on performance metrics