How Governance can Become the Growth Engine for Public Sector AI
Governance isn’t red tape. It’s the route to safe, scalable AI that actually reaches the frontline. This short but insightful article distils the key insights from our joint Manuka x Databricks webinar – showing how Unity Catalog can turn governance from a blocker into the growth engine for public sector AI.
Executive Summary
The article cuts through the governance paradox – where controls designed to de-risk AI end up de-valuing innovation – and shows how to build rock-solid foundations with Databricks Unity Catalog, then move to live, governed services with a clear route to scale.
You’ll also see how Manuka’s method takes you from policy to production, starting with outcomes and landing one catalyst use case you can build on – not a long list of ideas that never leave the slide deck.
What you'll learn:
In a short, focused read, the summary covers:In under an hour, our panel breaks down:
A clear view of the governance paradox
Why public sector AI is stuck in pilots – and how current governance and legacy systems are holding you back.
The role of Unity Catalog in fixing it
How a single governance layer for data and AI can sit above existing systems and unlock safer, faster progress.
A simple route from policy to production
The Manuka method: value-chain mapping, selecting a catalyst use case, and creating a reusable pattern for scale.
A concrete proof point
A real-world GenAI policy assistant example, with hard numbers on speed, cost and risk.
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Want to understand more?
If you do have time for the deeper dive, you can also watch the full 60-minute session, Are You De-Risking or De-Valuing Your AI? The Cost of Poor Governance, on demand here.