The AI Governance Practitioner Program: Track 1

Course 3: Governance through the AI Lifecycle

Learn how to implement governance throughout the AI lifecycle using the technique pioneered by Amazon of mechanisms that transform principles into measurable, adaptive practice. Building on the organisational foundations from Course 2, this course shifts from "who governs" to "how governance works" across every stage from ideation to retirement.

You'll explore systematic approaches for each lifecycle phase, understand how governance decisions cascade between stages, and learn how properly designed and specified mechanisms that are embedded into AI lifecycle can accelerate rather than impede AI development.

With a detailed walkthrough of activities across the AI Lifecycle and over 35 examplar Mechanism Cards and templates, you'll gain the tools to build governance that's automated, evidence-based, and continuously improving - creating value, not bureaucracy.
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 The next course in The AI Governance Practitioner Program is Course 4: Building the Policies and Business Case for AI Governance (due for release in December 2025). 

Level:

Practitioner

Inclusions:

10 Topics
74 Activities
35 Mechanism Cards

Time:

6.75 hours

(52 bite-size videos)

Maps to:

AIGP Domain I.B
AIGP Domain III.A

Your Guide

James Kavanagh

What You'll Learn

Embed governance into the AI lifecycle — learn from practices in Amazon of how to build adaptive mechanisms that support and accelerate safe innovation without adding adding bureaucracy

Understand governance activities in all 7 stages of the AI Lifecycle

From ideation through development, deployment and even retirement, learn what governance activities matter at each critical lifecycle stage.

Learn to apply Mechanisms thinking to AI Governance

Discover how Amazon applies the concept of mechanisms to turn good intent into consistent, adaptive governance that continuously improves.

Understand how to link organisational and system-level mechanisms

Connect portfolio-level capabilities like risk management with stage-specific mechanisms in order to efficiently scale your governance.

Adapt 35 exemplar Mechanism Cards to get a headstart in your organisation

See how mechanisms with clear inputs, outputs, tools, adoption and inspection methods are implemented in practice.

Course Outline

Your Guide

James Kavanagh

James Kavanagh is a globally recognised leader in AI Governance, known for designing and implementing governance frameworks that make AI safe, secure, and lawful in practice.

At Microsoft, he led the deployment of multiple Azure cloud regions worldwide, building governance mechanisms that scaled from infrastructure to security certification. At Amazon, he created and led the company’s first Responsible AI Governance Program, achieving one of the world’s first ISO 42001 certifications.

Today, as Founder and CEO of AI Career Pro, James helps professionals translate their existing experience into purposeful and rewarding roles in AI governance. He writes frequently to share his experiences and insights at blog.aicareer.pro.

He also leads the creation of Hooman, a platform that employs AI to help govern AI at scale through adaptive human oversight. Within the development lifecycle of Hooman, James and his team are testing every principle and framework taught in these courses in live, real-world systems. 

That’s the foundation of his philosophy: “We teach what we build, and we build what we teach.” Every course, case study, and tool in the AI Governance Foundation Program is grounded in real implementation experience - giving you access to the same methods used by leading organisations to govern AI safely, securely and lawfully.