Guests:

• Dara McCreary (Sapio Sciences)
• Anthony Chambers (Astrix)

Episode Overview:

In this episode, we dive into the practicalities of adopting AI in R&D and life sciences. Experts Dara McCreary and Anthony Chambers share insights on what AI can realistically replace, the barriers labs face, and how to implement AI without compromising reproducibility, traceability, or compliance.

Key Segments:

1. Adoption Challenges
   Breaking down the four major barriers to AI adoption:

• • Organizational: Lack of strategy and change management.
  • Technical: Weak infrastructure and poor integrations.
  • Data: Low quality and poor governance.
  • Regulatory: Gaps in traceability and compliance.

2. Getting Started: The Readiness Playbook
   Practical steps to kick off your AI journey:

• • Start small with a simple pilot task.
  • Pilot one workflow end-to-end with clear metrics.
  • Map and prioritize adjacent workflows.
  • Focus on data analysis with quality control gates.
  • Establish governance early for data, models, and compliance.

3. Industry Reality Check
   Common challenges when implementing AI:

• • Cultural resistance to AI adoption.
  • Reproducibility risks across multiple sites.
  • Technical debt caused by poor data standards and vendor lock-in.

Key Takeaways:

• Combine strong governance with a unified, AI-native platform.
• Start small, measure reproducibility, and scale proven workflows.
• Focus on traceability, data consolidation, and compliance to reduce risk.

Learn More: Discover how Astrix and Sapio Sciences can support your AI journey.