Whitepaper
The foundation for trustworthy AI: The FAIR data playbook for pharma
Most pharma AI systems can’t explain where their answers come from. This whitepaper sets out the practical path from fragmented data to grounded, auditable AI — built on FAIR principles and knowledge graphs.
Your AI is producing answers. Can you trace them back?
You’re working with AI that generates recommendations with apparent confidence. But when a regulator asks how it reached a conclusion, the evidence isn’t there. You can’t verify the data is current, or even what it is. That gap has real consequences.
For more than one in four life science professionals, the evidence chain that informs their AI system’s outputs is invisible.?
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This report is for you if…
You’re an R&D or data leader in pharma who needs AI that’s explainable and defensible — not just functional.
- You lead AI implementation and need outputs your organisation can verify and defend
- You’re responsible for compliance and need to understand what the EU AI Act requires of your data
- You work in drug discovery and want to see how knowledge graphs are being applied in practice
What you’ll discover
The practical case for grounded AI in pharma — what it requires, and what implementation actually looks like.
- Why 27% of life science professionals can’t trace the data their AI uses — and what FAIR principles do about it
- How knowledge graphs reduce hallucinations and create an auditable trail for every answer
- What implementation looks like across two scales: from a proof of concept to 350 million research records
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