Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, I watched a startup founder spend two hours deciding whether to approve a $50 marketing spend. Two hours. For fifty dollars. This wasn't an isolated incident – I've seen this decision paralysis everywhere: businesses drowning in endless micro-decisions that should take seconds, not hours.
Here's what nobody talks about: the real killer isn't making bad decisions. It's the decision fatigue that comes from making hundreds of tiny choices every day. By 3 PM, you're mentally exhausted from deciding font sizes, approval workflows, and budget allocations. The big strategic decisions? Those suffer because you've already burned through your mental capacity on stuff that shouldn't even hit your desk.
After working with dozens of startups and seeing this pattern repeatedly, I developed what I call the Decision Automation Framework – a systematic approach to identifying, categorizing, and automating the decisions that are eating your time and mental energy.
Here's what you'll learn from my experience implementing this across multiple client projects:
How to audit your daily decisions and identify automation opportunities
The 3-tier framework I use to categorize decisions by automation potential
Real automation workflows that eliminated 80% of routine business decisions
Why most decision automation fails (and how to avoid these pitfalls)
Platform comparisons and setup strategies that actually work
This isn't theoretical – it's based on actual implementations across SaaS startups, e-commerce stores, and service businesses. The results? SaaS founders who went from spending 4 hours daily on operational decisions to focusing entirely on growth strategy.
Industry Reality
What every business guru preaches about automation
Walk into any business automation conference and you'll hear the same tired advice: "automate everything!" The gurus make it sound simple – just throw some AI at your problems and watch the magic happen.
Here's the conventional wisdom everyone's pushing:
Start with the biggest processes first – tackle your most complex workflows
Use AI for everything – let machine learning handle all your decisions
Automate customer-facing processes immediately – prioritize external workflows
Invest in enterprise-grade solutions – buy the most expensive tools available
Automate for efficiency gains – focus on time savings as the primary metric
This advice exists because it sounds logical and sells expensive consulting packages. Complex automation projects require months of implementation, ongoing maintenance contracts, and sophisticated technical expertise.
But here's where this conventional wisdom falls apart in practice: you end up automating the wrong things. I've seen companies spend six months building complex customer journey automation while their internal team still manually processes every invoice, approval, and basic operational decision.
The real problem isn't that businesses lack automation – it's that they're automating the visible stuff while ignoring the decision fatigue that's actually killing productivity. Your CEO shouldn't be approving $30 software subscriptions while struggling to find mental energy for strategic planning.
The approach I developed focuses on decision relief, not process optimization. There's a difference, and understanding it changes everything about how you implement automation in your business.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I was working with a B2B startup that had raised their Series A. They had solid product-market fit, growing revenue, but the founders were completely burned out. Not from working on the product or talking to customers – from making hundreds of tiny operational decisions every day.
The CEO was personally approving every Zapier upgrade, every new team member's software access, every marketing experiment budget under $500. Meanwhile, they were postponing crucial strategic decisions because they were mentally exhausted by 2 PM.
I started tracking their daily decisions for a week. The results were shocking: 167 approval requests, 89 budget decisions, 45 workflow confirmations, and 23 vendor selections. All for amounts under $200 or processes that followed established patterns.
My first instinct was to build complex automation workflows. I spent two weeks creating elaborate Zapier sequences, setting up approval hierarchies in their project management tool, and building AI-powered decision trees. The result? A beautiful, sophisticated system that nobody used because it was too complex to implement.
That's when I realized I was solving the wrong problem. The issue wasn't that these decisions were hard to make – they were easy decisions that shouldn't require human input at all. A $50 software subscription for a tool they already used elsewhere? That should be automatic. Approving a blog post that follows their established content guidelines? Automatic.
The breakthrough came when I shifted from "automating processes" to "eliminating decisions." Instead of building workflows to help humans make decisions faster, I focused on identifying decisions that could be completely removed from human oversight.
This wasn't about AI making smart choices – it was about creating clear rules that eliminated the need for choices in the first place.
Here's my playbook
What I ended up doing and the results.
I developed what I call the Decision Elimination Audit – a systematic approach to identifying and categorizing every decision that hits your desk. Here's the exact framework I implemented:
Phase 1: Decision Tracking
For one week, every founder and key team member tracked every decision request they received. Not just the big stuff – everything. Slack approvals, budget confirmations, process clarifications, vendor selections.
The tracking sheet had four columns:
Decision type – What was being decided
Context needed – What information was required
Decision criteria – How they actually made the choice
Time invested – Including research and back-and-forth communication
Phase 2: The Three-Tier Categorization
After collecting data, I sorted every decision into three categories:
Tier 1 - Eliminate Completely: Decisions with clear, repeatable criteria that never require human judgment. Examples: Software subscriptions under $100/month for approved tools, content approval for posts following style guides, hiring contractors from pre-vetted lists.
Tier 2 - Delegate with Rules: Decisions that need human input but don't require founder-level attention. Examples: Marketing experiment budgets under $1000, customer support policy clarifications, vendor negotiations within budget parameters.
Tier 3 - Keep and Improve: Strategic decisions that require founder judgment but can be improved with better information. Examples: Product roadmap priorities, hiring key team members, partnership opportunities.
Phase 3: Building Elimination Workflows
For Tier 1 decisions, I created automatic approval systems using a combination of Zapier, budget tracking spreadsheets, and clear policy documentation. The key was making the automation invisible – team members submitted requests normally, but approvals happened automatically when criteria were met.
For the startup client, this meant setting up automatic approvals for:
Software subscriptions under $200/month from their approved vendor list
Marketing experiments under $500 that followed their testing framework
Content publication for pieces that passed their quality checklist
Team access grants for tools they already used elsewhere
The automation wasn't sophisticated AI – it was smart rules applied consistently. If a request met the criteria, it got approved automatically. If it didn't, it went to the appropriate team member (not the founder) for review.
Decision Audit
Track every choice hitting your desk for one week to identify elimination opportunities
Three-Tier System
Eliminate, Delegate, or Improve – categorize decisions by automation potential
Rule-Based Logic
Use clear criteria, not AI complexity, to automate approval workflows
Invisible Automation
Make automation seamless so team behavior doesn't need to change
The impact was immediate and measurable. Within two weeks of implementing the framework:
Decision Volume Reduction: The founder went from 167 weekly decision requests to 31 – an 81% reduction in operational choices requiring their attention.
Mental Energy Recovery: By eliminating decision fatigue, the founding team reported having clear mental energy for strategic work until 6 PM instead of burning out by 2 PM.
Team Velocity Increase: Projects that previously stalled waiting for approvals now moved forward automatically. Marketing experiments increased from 2 per month to 8 per month.
Process Compliance: Paradoxically, removing human oversight improved compliance. The automated systems enforced budget limits and policy guidelines more consistently than manual approvals.
The most surprising result? Better strategic decisions. When founders weren't exhausted from micro-decisions, they made more thoughtful choices about product direction, hiring, and growth strategy. Decision quality improved when decision quantity decreased.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights from implementing decision automation across multiple businesses:
Start with elimination, not optimization – Don't automate bad decisions faster; eliminate unnecessary decisions entirely
Rules beat AI for most business decisions – Clear criteria applied consistently outperform machine learning for routine choices
Decision fatigue kills strategic thinking – Protecting mental energy is more valuable than saving time
Invisible automation drives adoption – The best systems require no behavior change from team members
Track everything first – You can't optimize what you can't measure; audit before automating
Delegate doesn't mean eliminate – Some decisions need human input but not founder input
Start small and expand – Begin with obviously automatable decisions before tackling complex ones
What I'd do differently: I initially focused too much on building sophisticated workflows instead of simple rules. The most effective automations were often the simplest – if X criteria met, then Y approval granted. Complexity kills adoption.
This framework works best for businesses with repeatable processes and clear criteria. It's less effective for companies in constant pivot mode or those without established operational patterns.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Automate trial-to-paid upgrade approvals based on usage metrics
Set automatic budget approvals for tools under $X that integrate with your stack
Create rule-based content approval for product updates and feature announcements
Eliminate manual approval for customer success touchpoints with clear triggers
For your Ecommerce store
For E-commerce stores specifically:
Automate inventory reorder decisions based on sales velocity and lead times
Set rule-based pricing adjustments for promotions within margin parameters
Eliminate manual approval for product listings that meet quality guidelines
Create automatic customer service escalation based on order value and issue type