AI governance, in practice
AI governance is the set of owners, policies, and controls that decide how data and models may be used, who is accountable for the resulting decisions, and how those decisions can be explained after the fact. Across the Orlando Insights Podcast, the same pattern keeps repeating: the organisations that get value from AI treat governance as delivery work, not paperwork. These 6 conversations are where that came up most directly.
- Episodes
- 6 on this theme
- Host
- Henry Clavo, founder
What these episodes agree on
Ownership before tooling
Every guest working in a regulated or high-stakes environment named owners for data and decisions before adding platform controls.
Explainability is a record
Governance is judged later, in an audit or a post-incident review, so the trail matters as much as the model.
Adoption forces policy
Employees adopt AI tools faster than policy is written; the fix is guardrails people can actually follow.
Episodes on AI governance
- 43 min
Mastering Data Governance and AI Governance: Insights with Laurent Dressie
Governance starts with treating data as a product: named owners, published definitions, and quality measured before a model ever touches it.
Listen and read the notes - 32 min
Building Real AI Systems: Context Engineering & Digital Transformation
Context engineering is a governance concern — what a system is allowed to retrieve determines what it is allowed to say.
Listen and read the notes - 39 min
Building Decision Intelligence at Scale | AI Leadership for Regulated Industries
In regulated industries the governing question isn't whether the model is accurate, but whether the decision it informs can be explained and audited months later.
Listen and read the notes - 35 min
AI in Everyday Work | What Leaders Must Know About Digital Transformation
Once employees adopt AI tools informally, policy has to catch up: acceptable use, data boundaries, and review steps become governance work.
Listen and read the notes - 31 min
Melinda deHolle: Human Judgment in an AI World: Leadership Beyond the Algorithm
Good governance defines where a human stays accountable, rather than assuming automation removes the judgment call.
Listen and read the notes - 38 min
The Real Reason AI Fails | Future Technology Requires Better Training
Most failed AI programs fail on training, ownership, and process design — not on the algorithm. Governance is what surfaces that gap early.
Listen and read the notes
Where we do this work
The themes above are the same ones we implement for clients — governance that ships alongside the pipelines and models, not after them.
- AI Strategy & Governance
Policy, risk controls, model inventory, and an adoption roadmap your legal and security teams can sign off on.
- Enterprise Data Engineering
Governance only holds when the data layer does: contracts, lineage, and quality enforced at the source.
- Head of AI Practice
Leads LLM integration, intelligent automation, and AI governance engagements.
Put a governance model in place
Book a free 30-minute call. We'll review how AI is already being used in your organisation and where the accountability gaps sit.