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The FDA Group’s Nick Capman recently sat down with Catherine Lunardi, Founder and CEO of GenAIz, for a practical, grounded discussion on how life science organizations can successfully deploy artificial intelligence within GLP environments to build the “lab of the future.”
Rather than getting lost in AI hype, Catherine emphasizes a methodical approach: identifying the right challenges, aligning projects with strategic goals, validating technologies in regulated contexts, and—most importantly—keeping people in the loop.
Drawing on her experience leading GenAIz for nearly a decade and a career spanning global pharma IT leadership at GSK, consulting at CGI, and business analysis at Héma-Québec, Catherine explains how AI can break down data silos, accelerate insights, and support compliance—without replacing the human expertise at the heart of drug development and lab operations.
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Catherine’s key takeaways and recommendations
If you’re short on time, here are standout insights from this episode—especially useful for R&D, regulatory, and quality leaders considering AI in laboratory environments.
Start with the challenge, not the hype. Don’t adopt AI just because it’s trendy. Define the concrete problem to solve and ensure it aligns with your company’s strategy, whether that’s faster trial execution, higher quality, or growth.
Tackle data complexity first. Labs struggle with aggregating data across instruments and legacy systems. AI can help standardize formats, surface anomalies, and accelerate insights from millions of data points in early clinical phasesm
Think in states: manual → siloed → orchestrated. Many labs still rely on handwritten notebooks or semi-manual Excel processes. The future state is automated orchestration, with human-in-the-loop validation for anomalies and compliance checkpoints.
Go low and slow. Catherine recommends AI discovery assessments and pilots that target high-volume, repetitive, or low-ROI processes. This creates visible wins, builds trust, and lays the foundation for broader adoption.
Compliance is non-negotiable. AI systems must be validated, auditable, and explainable. In regulated settings, it’s often safer to “freeze” models than allow autonomous self-learning in production. Quality risk management and supplier qualification are essential.
Change management is everything. AI initiatives succeed or fail based on stakeholder adoption. Teams must understand the benefits, trust the technology, and see their own roles elevated—not diminished—by automation.
Humans remain central. AI augments people rather than replacing them. Catherine likens it to the washing machine: it didn’t eliminate work, it elevated standards of living and freed people for higher-value activities.
One thing to bring back to your team
Run a structured AI discovery exercise. Map your processes, assess risks, identify “low-hanging fruit” projects, and pilot solutions with strong leaders and clear ROI measures.
This creates momentum, builds internal literacy, and sets the foundation for a lab that’s both AI-enabled and regulation-ready
Catherine Lunardi is the Founder and CEO of GenAIz, a Montreal-based platform that leverages data orchestration and AI to accelerate drug development, optimize clinical trials, and support regulatory, quality, laboratory, and R&D teams. Since founding the company in 2016, she has guided its mission to break down silos, unlock insights, and empower life science organizations to work smarter, faster, and more cost-effectively.
Previously, she served in IT leadership roles at GSK in Belgium, consulted at CGI, and began her career as a business analyst at Héma-Québec. Catherine holds a B.A.A. in IT Management from HEC Montréal and regularly speaks on AI in life sciences, including at international forums such as the G7.
Connect with her on LinkedIn here.
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