What problem are you trying to solve?
When AI enters a workflow, it tends to expose problems that were always there. These are the most common patterns.
Why didn't AI reduce dependence on experts?
AI was supposed to reduce dependence on experts. Instead, your most experienced people are busier than ever. Output is up. Their availability isn't. This isn't a failure of implementation — it's three mechanisms operating together.
The pattern has a name: The Expertise Paradox
Follow this path →Why do AI systems miss things humans understand automatically?
The same files that worked fine for your team are producing errors when AI systems consume them. The documentation didn't get worse. The consumer changed.
The pattern has a name: Machine-Consumed Infrastructure
Follow this path →Why did a confident decision turn out to be wrong?
The output appeared sound. It survived review. Nothing seemed obviously wrong. Then something important turned out to be missing — not a hallucination, but a gap that the output gave no indication was there.
The pattern has a name: Plausibility Anchor
Follow this path →