This aligns with what we've seen in real-world automation projects. The model is rarely the reason things fail. More often it's unclear processes, weak integrations, or missing ownership.

The point about the "long tail" is especially important. Automating the easy 80% is straightforward. Designing for the messy 20% is where most of the engineering effort should go. A system that knows when to escalate to a human is usually more valuable than one that tries to automate everything.

At IT Path Solutions, we've found that successful AI automation projects start with process mapping and observability long before prompt tuning enters the conversation. The best automations aren't the most autonomous ones they're the ones that remain reliable when reality inevitably deviates from the happy path.

"Automation is a multiplier" is probably the key takeaway here. If the underlying process is broken, AI just helps you reach the wrong outcome faster.