Clarifying the Content of AI Governance

Clarifying the Content of AI Governance

- by Irina Steenbeek, Expert in Data Management

🔍 Insight Series – Issue 3

A lack of a common definition for “governance” remains a challenge across the data management community. To bring clarity, let’s ground our understanding in the definition offered by Merriam-Webster: governance is “the act or process of governing or overseeing the control and direction of something.”

In the context of AI, governance includes two core responsibilities:

📌 Exercising Authority

This refers to making high-level decisions that define the organization’s vision, values, and strategic direction for AI. It involves setting expectations, principles, and boundaries for how AI should be developed and used.

📌 Overseeing Control

This includes supervising how well AI-related activities align with established governance decisions—monitoring legal, ethical, and internal compliance, assessing the effectiveness of controls, and ensuring that AI supports broader organizational goals.

These two responsibilities—authority and control—manifest differently across organizational levels:

🏛️ Strategic Level – Set Direction & Commitment

  • Exercising authority: Executive leadership defines the organization’s AI vision, ethical principles, and acceptable risk levels. They approve the enterprise-wide AI strategy.
  • Overseeing control: Leadership monitors whether AI initiatives stay aligned with the approved strategic direction.

🛠️ Tactical Level – Translate Strategy into Action

  • Exercising authority: Department heads or functional managers approve AI use case roadmaps, allocate resources, and endorse standards for model development and deployment.
  • Overseeing control: Governance teams evaluate how well standards and policies are being implemented.

⚙️ Operational Level – Day-to-Day Execution

  • Exercising authority: Operational managers define and enforce procedures for model usage, monitoring, documentation, and escalation.
  • Overseeing control: They ensure AI systems operate as intended within approved parameters.

Governance is not just about rules—it’s about making decisions and making sure those decisions are followed. And when it comes to AI, clarity at all levels is more important than ever.