AI Governance That Enables Instead of Blocks
Most AI governance frameworks in biotech are designed primarily to prevent bad outcomes: data leakage, regulatory violations, hallucinated submissions, inappropriate use of patient data. This is necessary but insufficient. Governance frameworks designed only to prevent bad outcomes create friction that slows adoption and limits the value AI can create.
The better design principle: governance should make the right thing easy and the wrong thing hard, not make everything hard in the hope of preventing the wrong things.
The enabling governance framework
1. Classify data, not tools. Rather than maintaining a list of approved AI tools, classify your data into tiers (public, internal, confidential, regulated) and define which tiers can be used with which types of tools (internal deployment, cloud API with DPA, no AI use). This approach is tool-agnostic and doesn't require governance updates every time a new AI tool appears.
2. Maintain a pre-approved use case library. Rather than requiring individual approval for every AI application, develop a library of use cases that have been reviewed and approved for specific data tiers. Any use within an approved use case requires no additional review. Novel use cases are reviewed once and added to the library if approved.
3. Establish clear human review requirements by document type. Rather than requiring human review of all AI-generated content or none of it, define review requirements by document type and regulatory consequence: AI-generated first drafts of regulatory submissions require qualified professional review before submission; AI-generated internal summaries for internal use require no specific review protocol.
4. Build AI quality metrics into existing quality systems. AI outputs are not categorically different from other information sources — they have error rates that need to be tracked and managed. Integrate AI quality metrics into the existing quality management system rather than treating AI as a separate category.
What enabling governance looks like in practice
A regulatory affairs team operating under enabling governance can use AI for any approved use case without requesting permission. They know exactly which data they can use (pre-classified), what human review is required for their document type, and how to raise a question about a novel use case. They spend their time on the work, not on compliance navigation.
That's the target state.