AI Governance in Pharma R&D

What if the success of AI in pharma R&D depends less on how powerful the models are—and more on whether companies build the right guardrails around them?

The pharmaceutical industry has moved beyond AI experimentation. We're seeing billion-dollar partnerships reshape drug discovery, with deals like AstraZeneca's $840M investment in Verge Genomics and Merck's $594M collaboration with BenevolentAI.

The question isn't whether AI will transform R&D—it's whether companies can build trust infrastructure fast enough to harness its potential responsibly.

🔬 AI RESHAPING THE R&D PIPELINE

• Target Discovery: Pfizer adopted Google Cloud's AI Target and Lead ID Suite for rapid therapeutic candidate identification

• Rare Disease Focus: AstraZeneca's Alexion unit leveraging ML to mine patient tissue data for novel drug targets ($840M deal)

• Generative Molecule Design: AstraZeneca + Absci co-designing antibody candidates via genAI platform ($247M valuation)

• Small Molecule Optimization: Merck + BenevolentAI generating and refining drug leads through AI ($594M collaboration)

• Clinical Operations: Novartis generated 10,000+ clinical study reports using AI in 2023

• Patient Recruitment: Sanofi's "plai" platform optimizes trial site selection based on patient convenience and diversity targets

  
  

🏛 GOVERNANCE FRAMEWORKS AS DIFFERENTIATOR

• Human-in-the-Loop: Top pharma companies are embedding human oversight in R&D AI workflows, in line with FDA guidance on model credibility

• Risk Classification: Pharma regulators like the EMA now call for risk-based governance of AI across the R&D lifecycle, prompting companies to assess use cases based on context, sensitivity, and data quality

• Cautionary Tales: IBM Watson for Oncology discontinued in 2023 due to unexplainable outputs

• ROI Scrutiny: Bayer ended deal with Exscientia over performance concerns—even early-stage R&D tools face rigorous evaluation

  
  

📋 REGULATORY EVOLUTION

• FDA Framework: 2025 draft guidance proposes AI "credibility framework" requiring context-of-use validation for any AI-informed data in submissions

• EMA Endorsement: 2024 reflection paper supports AI use from discovery to post-approval stages with risk-based oversight

• Shift in Approach: Moving from blanket skepticism to structured acceptance with proper validation requirements

  
  

🚧 THE REALITY CHECK

• Executive Skepticism: 83% of pharma executives still view AI as "overrated" in high-risk discovery/trial areas

• Core Challenge: Balancing innovation potential with patient safety imperatives

• Success Pattern: Winners prioritize explainability over black-box performance, establish clear validation protocols, maintain human oversight

  
  

The pattern is emerging: Companies succeeding in AI adoption treat it as a governed business capability, not a tech experiment. As regulatory frameworks mature, pharmaceutical AI is transitioning from experimental frontier to operational reality.

What's your experience with AI governance in pharma R&D? Are we moving too fast, too slow, or finding the right balance between innovation and safety?