Hello and welcome, especially to all new subscribers.

Quick announcement: I'm launching something new soon that helps small businesses cut through AI hype and implement tools that actually move the needle.

But first, I need to test my frameworks in the real world.

If you run a small business and have specific, tangible tasks eating up your time – the kind where you think "there has to be a better way" – I want to hear about it.

I'm selecting a few subscribers for free consulting sessions to stress-test my approach. Hit reply and tell me what's keeping you busy that shouldn't be.

PS, you can also find me over on LinkedIn sharing thinks throughout the week. Feel free to connect and say hi!

Let’s move on to today’s edition of The Inside Track:

💡 One Idea

Don’t Waste Your Time on Automation Theater

YouTube is drowning in AI automation videos. Fully autonomous LinkedIn content creators. Self-running sales outreach machines. Workflows that promise to eliminate human involvement entirely.

Most of it is theater.

Here's what separates useful AI implementation from automation porn: you need to understand the pain before you automate it away.

The Pain-First Principle

The most effective AI adopters I know follow a simple rule: solve it manually first. They either experience the problem directly or work closely enough with the process to understand its true friction points.

This isn't just about knowing what to automate—it's about understanding the environment, the edge cases, and the quality standards that matter.

When Impressive Beats Useful

I've fallen into this trap myself. I built a content automation engine that could theoretically run my social media presence.

Did it work? Technically, yes. The automation functioned perfectly.

Was it useful? No. The output was generic AI slop—flat, predictable, and completely lacking the voice that makes content worth reading.

The Better Path

Instead of full automation, I found something more valuable: AI as a writing partner. It handles the heavy lifting while I maintain creative control and voice.

This is the pattern that actually works: automate the mechanics, not the thinking.

Skip the viral automation tutorials. Start with small experiments on problems you actually face. The most powerful AI applications often look boring from the outside—they just happen to save enormous amounts of time for the people who use them.

The goal isn't to impress other AI enthusiasts. It's to create sustainable productivity gains in your actual work.

Share Inside Track with a friend. It helps them get ahead, and it helps me grow the newsletter to bring even more value to you.

🛠️ One Tactic

The 4-Step Method for Bulletproof AI Workflows

Once you've identified a high-potential task using frameworks like the Automation Potential Matrix, the real work begins: designing an automation that actually delivers.

Here's the problem most people encounter: they jump straight into tool selection and building without proper planning. The result? Automations that work in theory but break down when they encounter real-world complexity. Or, you just fall down a rabbit hole and waste hours on hours.

I've developed a 4-step framework that addresses this head-on. Let me walk you through it using a recent project: building a research assistant for this newsletter.

The Automation Challenge

I wanted to create an agent that would:

• Subscribe to relevant industry newsletters

• Read through new emails weekly

• Evaluate ideas for Inside Track angles

• Generate outlines and idea starters

• Alert me when complete

Step 1: Describe the Outcome

Start by documenting exactly what success looks like. Not just what the automation does, but the specific value it creates.

For my research assistant, I detailed not just the process, but the quality standards: what makes an idea "Inside Track-worthy," how comprehensive the analysis should be, and what format works best for my workflow.

This forces you to think beyond the mechanical steps to the actual business impact.

Step 2: Sketch the Flow

Visual mapping is where hidden complexity reveals itself. I use TLDraw (free and intuitive) to create a flowchart showing each automation step.

This isn't just documentation – it's discovery. You'll spot decision points, potential failure modes, and integration challenges that weren't obvious in your initial concept.

Step 3: Add Context and Nuance

Here's where most automations succeed or fail. When humans perform tasks, we make countless micro-decisions based on context and experience. What seems like an "obvious" choice between options A and B is only obvious because of accumulated knowledge.

For each step in your flow, document: • Edge cases and exceptions • Decision criteria and thresholds • Context that influences choices • Quality standards and guardrails

This nuance layer is what transforms a brittle script into a robust automation.

Step 4: Collaborate with AI

Once you have a complete brief, use AI to pressure-test your plan. I export my documentation as a PDF and prompt ChatGPT:

You're my business automation workflow engineer. Review this brief and provide: 
1) Suggestions to make this smoother/better/more valuable, 
2) A detailed implementation plan for N8N.

AI collaboration catches blind spots, suggests optimizations, and often reveals simpler approaches you hadn't considered.

The Compound Effect

This planning framework might seem like overhead, but it pays massive dividends. Well-planned automations run reliably, require minimal maintenance, and often exceed their original scope as you identify new optimization opportunities.

The goal isn't just to automate one task – it's to develop the systematic thinking that lets you automate anything.

Here’s my full brief, flow chart and AI response to the automation plan I just described.

Good luck!

That’s it for today. Thanks!

– Martin