When I started working with a new client recently, we were excited to jump into AI use cases. I suggested we begin with a governance policy. Their response? “Oh no, that’ll take us six months. Let’s not even start then.”

That kind of reaction is common—and understandable. The word "policy" often triggers visions of lengthy legal reviews and red tape. But here’s the truth: governance doesn’t have to be heavy-handed to be effective.

In this particular case, we launched with a simple three-point policy:

• Don’t use AI tools with confidential or sensitive company data.
• Don’t upload personally identifiable information (PII).
• Use your best judgment when experimenting with AI.

That was enough to unlock experimentation safely—and avoid derailing innovation with premature complexity. Is this perfect for the long term? Probably not. But for a fast, responsible start? Absolutely.

Every organization will require a different approach. For some, that simple policy may be enough. For others—especially those subject to internal audits, regulatory scrutiny, or handling customer data—more robust governance is non-negotiable.

Before you scale, ask:

👉 What level of liability or auditability applies to our AI use?

👉 Do our AI systems need to pass internal or external checks?

Because AI systems can behave like black boxes—difficult to audit or explain—knowing your risk level upfront will shape how deeply you need to invest in governance from day one.