Many organizations already have the beginnings of an AI governance program. There’s an acceptable use policy, a responsible AI statement, maybe a committee that meets quarterly to talk about
Organizations don’t struggle to manage their data because they lack policy. Most have a retention schedule somewhere, a records management program with a name and a budget
Governance is often measured by what organizations define. Policies are written. Retention schedules are approved. Procedures are documented. Controls are established. But governance is not proven through
Most organizations have retention schedules. Many have documented policies, established governance frameworks, and clearly defined retention requirements. Yet when it comes to disposition, the story often changes.
A retention schedule is not a one-time deliverable. At least, it shouldn’t be. Many organizations invest significant time defining retention categories, aligning legal and regulatory requirements, and publishing formal schedules. Once
Retention is difficult to apply within a single environment. It becomes significantly more complex across multiple jurisdictions. Many organizations today operate across regions, countries, and
Most retention schedules are designed to be consistent. Categories are defined centrally. Legal and regulatory requirements are mapped carefully. The goal is clear: similar information
A retention schedule can be well designed, carefully maintained, and fully aligned with legal and regulatory requirements. And still not be applied. This is the point where
Every organization has policies. Retention schedules are written. Governance frameworks are documented. Procedures describe how information should be managed. But policies alone do not create
Technology is changing quickly. The role of information governance professionals is changing with it. For many years, information governance and records management programs focused primarily