Introduction

AI value at scale depends on consistent governance from intake to monitoring, not just model development. This section equips companies to standardise how use cases are proposed, approved, built, deployed, and managed over time. You’ll learn how to set stage gates, documentation expectations, and metrics so delivery teams can execute faster with clearer accountability. The focus is practical governance that supports strategy execution while managing risk across the AI lifecycle.

Learning Objectives

• Define intake criteria and minimum evidence required to approve an AI use case for discovery.

• Apply stage gates to make consistent decisions across discovery, build, deploy, and monitor phases.

• Specify lifecycle documentation, KPIs, and ownership to ensure accountability and ongoing performance.