After working on dozens of AI automation projects, the same patterns keep causing failures. Here's what actually goes wrong — and how to avoid it.
After building 50+ automation systems for businesses of all sizes, I've noticed the same failure patterns repeating themselves. Not because the technology doesn't work — but because of how people approach it.
Everyone focuses on the fun AI part (the 20%) and underestimates data preparation, error handling, and integration work (the 80%).
Your ChatGPT integration works great in testing, then breaks on edge cases in production.
The fix: Spend 80% of your time on the boring stuff. Data cleaning, error handling, edge cases, and monitoring. The AI is the easy part.
"Can we also automate X?" becomes the project killer.
You start with "automate invoice processing" and end up trying to build an entire ERP system. The original project never ships because it keeps growing.
The fix: Define the scope in writing before you start. Lock it. When someone asks "can we also..." the answer is "yes, in phase 2."
No automation is fire-and-forget. APIs change. Data formats shift. Edge cases appear. Business rules evolve.
The companies that succeed with automation budget 15-20% of the build cost annually for maintenance. The ones that don't end up with broken systems and broken trust.
The most dangerous automation is one with no escape hatch. When (not if) the AI gets confused, there needs to be a clear path to a human.
The fix: Every automation should have:
If your manual process is chaotic, automating it just gives you automated chaos — faster.
The fix: Fix the process first. Map it out. Identify bottlenecks. Simplify. THEN automate.
The golden rule: never automate a process you haven't done manually at least 5 times. If you can't write down the logic step-by-step on a piece of paper, you can't automate it.
Start small. Ship fast. Iterate based on real data.