Why Biotech Teams Need AI Governance Before AI Tools
Most biotech VPs are rushing to adopt AI tools. The smart ones are building governance frameworks first. Here's why that distinction matters for reducing risk.
Most biotech VPs I talk to have the same concern: “We need to adopt AI or we’ll fall behind.”
They’re right. But they’re solving the wrong problem first.
The Real Risk Isn’t Missing Out - It’s Moving Wrong
When I audit AI implementations at biotech companies, I see the same pattern:
- Team adopts ChatGPT/Claude for “productivity”
- Someone pastes proprietary compound data into a prompt
- Legal finds out 3 months later
- Panic ensues
The irony? These teams were trying to be innovative. They just skipped the governance step that would have made innovation sustainable.
What Governance Actually Means
I’m not talking about 200-page policy documents that no one reads.
Effective AI governance for biotech means:
- Clear boundaries: What data can touch AI systems? What can’t?
- Audit trails: Can you prove compliance if regulators ask?
- Team alignment: Does everyone understand the rules, or just the compliance officer?
The 3-Hour Framework
Here’s what I cover in my AI Readiness sessions with biotech teams:
- Risk mapping (60 min): Where are your data sensitivity boundaries?
- Tool evaluation (60 min): Which AI tools meet your compliance requirements?
- Rollout planning (60 min): How do you enable adoption without enabling chaos?
The output is a one-page governance framework your team can actually follow.
The Bottom Line
AI adoption without governance isn’t innovation - it’s liability.
The teams that win will be the ones who built the guardrails before they hit the gas.
Ready to build your AI governance framework? Book an AI Readiness call - 30 minutes to assess where you are and what you need.