I asked one of the coaches in my Accelerator what one of his biggest tips is for AI builders, and he shared this. I couldn’t agree more. Most automation problems don’t come from bad tools… They come from developers trying to automate a process they’ve never actually understood. In engineering, this is the equivalent of trying to optimize code before you know what it’s supposed to do. And the outcome is always the same: ⚠️ Fragile workflows ⚠️ Random failures ⚠️ Endless debugging ⚠️ Clients who “don’t trust AI” Here’s the mindset shift that will save you hours of dev work: 1. Map the system before you automate it You’re not automating tasks — you’re automating decisions. So you need to know: - What triggers the process? - What information is required? - What decisions are being made (and why)? - What the failure modes are - What the actual desired output is Most devs skip this step. The elite ones don’t. 2. Ask: “Where does this process break under stress?” Every system has choke points: - Missing data - Human approval bottlenecks - Fuzzy criteria - Exceptions that aren’t documented Good automation doesn’t ignore edge cases. It designs around them. 3. Build the simplest, dumbest, most reliable version first People love jumping to multi-agent systems, fancy logic chains, or 40-step workflows. But the strongest automation is built like good software: - Simple - Testable - Modular - Predictable If the simple version doesn’t work, the complex one never will. 4. Only automate the pieces that are stable If a business process changes every two weeks, don’t automate it yet. You’re just building technical debt with fancy wrappers. Automate stable workflows. Assist unstable ones. Bottom line: Automation isn’t about being clever. It’s about understanding a system so well that you can design a version of it that never breaks. That’s what separates “workflow builders” from true automation engineers.