From Policy to Practice: Making AI Governance Work

By now, most organisations recognise the need for AI governance. Policies are written, principles are published, and awareness is growing.

Yet many organisations struggle with the same challenge: AI governance exists on paper, but not in practice.

This final article in the series focuses on how organisations can move beyond policies and frameworks to embed AI governance into everyday operations — where it actually reduces risk, supports compliance, and enables responsible innovation.

Building an AI Governance Operating Model

Understanding AI governance is one thing. Making it work in day-to-day operations is another.

Many organisations publish AI principles or policies but struggle to embed them into real decision-making. The result is inconsistent adoption, unclear accountability, and AI use that bypasses intended controls.

This article explains how to design an AI governance operating model — the practical structure of roles, processes, and controls that turns governance from theory into everyday practice.

AI Governance Across the Information Lifecycle

AI systems do not operate in isolation. They consume information, generate new information, influence decisions, and leave behind evidence that organisations may need to retain, explain, or defend.

For this reason, effective AI governance must extend across the entire information lifecycle — not just the point at which an AI tool is deployed.

This article explains how AI governance aligns naturally with information governance and records management by controlling AI across each lifecycle stage: creation, use, storage, decision-making, retention, and disposal.

The EU AI Act: What Organisations Outside Europe Need to Know

The EU AI Act represents the most comprehensive attempt to regulate artificial intelligence anywhere in the world. While it is European legislation, its impact extends far beyond Europe’s borders.

If your organisation develops, deploys, sells, or uses AI systems that affect people or markets in the European Union, the EU AI Act may apply to you — even if you are based elsewhere.

This article explains when the EU AI Act applies to non-EU organisations, how its risk-based approach works, and why strong AI governance is the most practical way to prepare.

The EU AI Act: What Organisations Outside Europe Need to Know

The EU AI Act is now a reality — and it matters even if your organisation is not based in Europe.

Why? Because the Act is designed to regulate AI systems placed on the EU market and, in many cases, AI systems whose use affects people in the EU. For organisations that sell into Europe, support EU-based customers, operate global platforms, or deploy AI in HR, finance, or customer processes, the EU AI Act can become a material compliance requirement.

This article explains the Act at a practical level: when it applies to non-EU organisations, how its risk-based model works, what high-risk means, key compliance timelines, and what you can do now to prepare through AI governance.

AI Governance Frameworks: What Exists, What Works, and What’s Still Missing

As AI adoption accelerates, many organisations ask a sensible question: Which AI governance framework should we use?

The challenge is that “framework” can mean different things — principles, standards, risk models, or regulation — and many frameworks are complementary rather than competing.

This article provides a practical overview of the leading AI governance frameworks and approaches in use today, what they typically cover, where gaps often remain, and how to use frameworks to build governance that is workable, auditable, and aligned to information governance.