AI can improve developer output without proving that the capability previously developed through that work is still being formed. The contradiction is that AI gives management better evidence that work can be removed before it gives management equivalent evidence that the capability produced through that work can be removed too.
An AI system can become easier for people to understand without making them better at recognizing when its recommendation is wrong. Do not accept explanation availability or the presence of an override mechanism as evidence that effective human oversight exists.
Giving an AI agent access to more enterprise data can remove a real constraint; but it does not, by itself, show that the agent is more ready to make or execute decisions.
AI coding agents need repository context, but the emerging evidence does not support a simple rule that more (or less) persistent context produces better results.
Do not simply replace a usage metric with a single outcome metric. For consequential decisions, require an evidence bundle combining the intended workflow outcome with measures showing where cost, delay, rework, review effort, quality, or risk moved.
AI-assisted code may be faster to produce, and current academic evidence does not establish that it is inherently less maintainable than human-authored code. What the evidence does show is that functional success, static quality, and downstream maintainability are different questions answered at different points in the software lifecycle.
