Introduction
AI and machine learning dominate biotech headlines. They’re pitched as silver bullets—from discovery to clinical trials. But if you’ve lived inside Pharma IT or led digital transformations, you know the truth: AI isn’t a revolution. It’s an evolution. And evolution only works when the environment is ready.
After 25+ years leading IT across Research, Development, and Commercial functions, I’ve seen the cycle repeat. The need for innovation hasn’t changed. What’s changed is the infrastructure: more compute, more data, better tools. Yet the core challenges remain.
Core Challenges
Despite the hype, success still comes down to fundamentals:
- Clean, connected, governed data.
- Security, permissions, and compliance.
- Robust information architecture.
Without these, even the smartest model fails. No AI can paper over a broken data foundation—and the cost of ignoring this reality is wasted time, money, and trust.
Semantic Data Models: An Old Idea, a New Edge
What’s exciting today isn’t entirely new. In the early 2000s at Pfizer, I worked with ontologies and semantic data models—using triples to define relationships:
- TP53 — regulates — Cell Cycle
- BRCA1 — associated_with — Breast Cancer
- Aspirin — inhibits — COX1
These simple triples form knowledge graphs, which are now powering drug discovery and clinical decision support.
The new capability: LLMs can now extract triples from unstructured text and align them to formal ontologies. This bridges natural language with structured knowledge, creating intuitive interfaces and advanced reasoning systems. It’s a powerful addition to the biotech data toolkit.ces to advanced reasoning systems. It’s a powerful new capability in the biotech data toolkit.
Figure: This is a high-level architecture. It shows how LLMs interact with triples, ontologies, and knowledge graphs. They support AI reasoning in life sciences.
Big Data, Content, and Governance
AI needs fuel—large, diverse, high-quality datasets. It also needs access to unstructured content: documents, reports, emails. Yet many organizations still operate with fragmented, poorly indexed data.
The tension is real: innovation vs. control. AI can expose sensitive information as easily as it can surface insights. That’s why security, governance, and trust frameworks must scale alongside AI.
Modern AI helps—NLP can extract structure, AutoML can flag quality issues—but even the best tools require a solid foundation. As one analysis put it: “Without well-organized information, AI-driven insights skew, search fails, and knowledge-sharing initiatives collapse.”
Big Pharma vs. Small Biotech
I’ve worked in both worlds. Each has strengths—and blind spots:
Big Pharma
- Scale, data, mature infrastructure.
- But siloed systems, legacy baggage, and cultural resistance.
- Opportunity: responsibly scale AI across workflows.
Small Biotech
- Nimble, cloud-native, therapeutic focus.
- But limited datasets and compliance maturity.
- Opportunity: build smart, unified ecosystems from the ground up.
In smaller organizations, I’ve built secure digital platforms integrating tools like Benchling, Egnyte, and Microsoft 365 to support both regulated and non-regulated domains. Agility is the edge.pport both regulated and non-regulated domains.
Risk, Opportunity, and Leadership
This moment is full of potential—but also risk. If AI is bolted onto broken foundations, it won’t scale. If organizations invest in semantic data, governance, and interoperability, AI can deliver trusted, explainable, and resilient outcomes.
That requires more than data scientists. It demands digital leadership: leaders who can align data, compliance, and innovation.
Conclusion
AI in life sciences isn’t magic. It’s the next step in a long evolution of data and systems. The winners will be those who architect for intelligence—semantic, explainable, and scalable.
This is our moment to lead. Let’s not chase hype. Let’s build the foundations that turn information into lasting advantage.