AI governance at scale: Build, extend or fall behind

AI adoption is accelerating. But as more models, datasets and workflows enter your environment, your governance approach either scales with them or becomes a liability. McKinsey research shows 51% of organizations experienced at least one negative AI consequence in the past year. The question is no longer whether to govern AI, but whether your current approach can handle what comes next.

Read this guide to evaluate your path forward and gain a deeper understanding of:

• The true operational cost of building versus extending
• AI governance A four-phase framework for inventorying, controlling, delivering and observing AI assets 
• Which capabilities reduce exposure to model drift, data bias and audit risk