There Is No One-Size-Fits-All AI Governance
AI governance is no longer just a policy discussion.
It is becoming a practical question for organizations:
How should we actually govern AI in our own business?
Many organizations begin by looking for an AI governance framework to adopt. This is understandable. Frameworks provide structure, language, and a starting point.
But the more important question is not simply:
Which framework should we use?
It is:
Does this governance approach truly fit our organization?
One of the recurring themes I took away from the IAPP Asia Forum 2026 was clear:
There is no one-size-fits-all approach to AI governance.
This matters because AI risk does not appear in the same way across every organization.
A bank, an insurance company, a securities firm, a hospital, a technology company, and a public agency may all use AI.
But they do not face the same risks.
They do not operate under the same regulatory expectations.
They do not have the same data environment, decision-making structure, customer impact, operational maturity, or risk appetite.
From my experience as a lawyer, compliance professional, and former operations executive, I have seen this clearly in other regulated areas.
Insurance is not banking.
Banking is not securities.
Securities is not healthcare.
The same principle applies to AI governance.
A useful AI governance framework should help an organization ask the right questions, not simply produce the same answers as everyone else.
It should consider:
- Industry context
- Regulatory exposure
- AI use cases
- Data sensitivity
- Human oversight
- Operational maturity
- Business objectives
- Accountability structure
Good governance is not created by copying another organization’s framework.
It is created by translating trusted principles into practical controls that fit the organization’s real risks and responsibilities. International frameworks and standards are important. They help establish common language and direction.
But implementation must be contextual.
For boards and senior leaders, the key question should not be:
Do we have an AI governance framework?
The better question is:
Have we designed an AI governance approach that fits how our organization actually uses AI?
Because in the age of AI agents, automated decision-making, and increasingly complex digital operations, governance that only looks good on paper may create a false sense of confidence.
Good AI governance should never be copied.
It should be designed.
