Building an AI-ready public workforce

OECD full report on how public-sector workforces are (and are not) prepared to deploy AI. Brought into the library as a governance-piece anchor: the argument is that whether an AI system is capable *in practice* depends on the institutional scaffolding around its use, not only on the model or the harness.

Counterweight to the software-centric pole of the library. A large portion of real AI deployment lives inside institutions whose capability depends on workforce preparation, training, accountability, and procurement — none of which is captured by 'harness' in the coding-agent sense.

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A governance entry. Places the question of AI capability-in-practice inside the frame of public administration: whether AI makes a public-sector system more capable depends on training, data integration, procurement norms, and public-private partnership structures, not only on the model or its harness. The OECD framing forces the library to reckon with a kind of scaffolding that coding-agent practitioners rarely name: • Workforce training as a first-mile input-formation mechanism. • Accountability procedures as a ratification harness with legal and political standing. • Cross-agency data integration as a grounding-and-context-loading substrate. Why it pairs with the software entries • Tan, thin harness / fat skills — highlights the domain mismatch: thin-harness prescriptions assume a software-practitioner user. Here the "user" is a multi-layered public institution. • HumanLayer, "Skill Issue" — both pieces agree that the harness matters; they disagree about which harness.

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