As AI coding tools and agentic workflows become embedded in software delivery, CIOs need to govern AI spend by business value, workflow impact, and platform dependency. Not by seats, prompts, requests, or tokens alone.
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.
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