⚡ Quick Summary
This paper presents a practical, governance-first approach to AI adoption in the public sector, grounded in a real-world GenAI proof-of-concept delivered by the Geological Survey of Queensland. Instead of focusing on models or tooling, it reframes AI adoption as an organisational capability challenge. The core contribution is a dual-framework model: an AI Readiness Assessment Framework and a lifecycle-aligned AI Governance Framework. Together, they help organisations assess whether they are actually prepared to deploy AI responsibly, and how governance artefacts, roles, and controls should scale with risk, maturity, and use case scope. The paper is especially strong in translating abstract “responsible AI” principles into operational plans, roles, and artefacts that can survive beyond pilots and into production environments.
🧩 What’s Covered
The document is structured around a journey from experimentation to production-grade AI services. It begins with context: a Generative AI proof-of-concept (“Digital Librarian”) designed to improve discovery across large volumes of geological data. While technically successful, the PoC exposed a familiar gap: strong data and tooling, but weak organisational and governance readiness.
From this, the authors develop an AI Readiness Assessment Framework built around four core domains: Strategy, Organisation, Data, and Technology. These domains are deliberately interdependent and visualised through both a Venn model and a radar (spider) diagram. The framework emphasises that AI readiness is not linear and that weaknesses in strategy, leadership, or data governance can block scaling even when technology performs well.
The second major component is the AI Governance Framework, aligned to six AI lifecycle stages (Plan & Design through Operate & Monitor). Governance artefacts are mapped to lifecycle stages and scaled using four risk-based scopes, from low-risk internal GenAI use to high-risk, public-facing, multimodal systems. The paper details concrete governance artefacts such as AI strategy, risk and impact plans, data ethics strategies, human oversight frameworks, and assurance plans.
Importantly, the governance model is embedded into existing enterprise, IT, and data governance structures rather than treated as a standalone AI function. Detailed appendices define governance roles, accountability chains, and readiness assessment criteria, making the framework directly implementable rather than aspirational.
💡 Why it matters?
This paper addresses one of the biggest failures in real-world AI programmes: mistaking technical success for organisational readiness. By explicitly separating AI capability from AI readiness, it gives leaders a way to explain why pilots stall and what needs to change to scale responsibly. The lifecycle- and risk-proportionate approach anticipates regulatory expectations under regimes like the EU AI Act, without being framed as compliance-first. For governance, risk, and policy professionals, it offers a rare bridge between high-level AI ethics principles and day-to-day operational controls.
❓ What’s Missing
The framework is intentionally high-level and not a full diagnostic tool. While assessment criteria are provided, organisations looking for quantitative scoring or benchmarking guidance will need to extend it further. The public-sector focus also means that private-sector incentives, procurement dynamics, and vendor governance receive limited attention. There is also limited discussion of foundation model dependency risks or cross-border data and model governance, which are increasingly relevant for GenAI deployments.
👥 Best For
Public sector organisations, regulators, and data-intensive agencies moving from AI pilots to production. AI governance leads, chief data officers, enterprise architects, and policy teams seeking a structured way to operationalise responsible AI without reinventing governance from scratch. Also valuable for large enterprises with complex data estates and low tolerance for AI risk.
📄 Source Details
White paper authored by Dr Jia-Urnn Lee, Gavin Kennedy (FrontierSI), Mark Gordon, Dr Rob Chatterjee, and Steven Bowden (Geological Survey of Queensland). Published 2025 under a Creative Commons CC BY 4.0 licence.
📝 Thanks to
Geological Survey of Queensland, FrontierSI, and contributors from the UNSW AI Institute for openly sharing a rare, implementation-focused view of AI governance in practice.
