Oversight, control, rules-as-data, risk, accountability in AI systems
AI governance
I walk into firms with AI policies sitting in SharePoint. They get cited in board decks. They show up in audit binders. Then a model makes a decision, moves money, drafts client output, and the policy watches from the shelf. That gap is a wiring problem. Prose does not intercept
AI governance
TL;DR: Organizations buy AI without defining what success looks like. They deploy tools, run pilots, create governance frameworks, but skip the step of connecting technology to measurable outcomes. The result is expensive theater that produces activity without results. Why most AI initiatives fail: * No measurable outcome defined before deployment
Agent design
Prose specifications can't be verified. A constitutional stack needs a runtime, and a runtime needs structured data.
AI adoption
I've watched the same pattern repeat across dozens of organizations in the past 18 months. A leadership team sees a demo. Someone in the room plays with ChatGPT over lunch. The conversation shifts from "should we" to "how fast can we" in a single