Evolve25: Fireside chat with Brian Martin From AbbVie
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AbbVie Chief Architect Brian Martin joins Cloudera’s Abhas Ricky to reveal how the pharmaceutical giant is shifting from qualitative to quantitative R&D decision-making using AI.
Discover how AbbVie leverages Knowledge Graphs and RAG (Retrieval-Augmented Generation) to automate clinical study reports, reducing draft times by up to 80%. Learn about their "three-class formalism" for human-in-the-loop governance and how they achieved a 250,000-hour FTE return on investment in 2024.
This session provides a strategic roadmap for scaling AI in highly regulated industries while maintaining strict data security and clinical integrity.
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Chapters:
00:00 Intro: Meet Brian Martin, Chief AI Architect at AbbVie
00:46 Three Pillars of Enterprise GenAI: Use Cases, Metrics, and Takeaways
01:32 Qualitative to Quantitative: How LLMs unlock R&D data value
02:20 The Pharmaceutical R&D Pipeline: From discovery to market access
03:07 Building Knowledge Graphs: Supplying quantitative evidence to medical experts
04:04 RAG and Graph Relationships: Powering the next generation of AI use cases
04:23 Automated Document Generation: Shrinking weeks of work into first drafts
04:54 Case Study: Accelerating FDA regulatory submissions with AI
05:46 Measuring Success: Operational, Analytical, and Experiential ROI
06:41 AI as a Team Sport: How to evaluate strategic technology partners
07:28 Curing Pilot-itis: Shifting from tech-driven to business-driven AI
08:28 Service Now Integration: Connecting configuration management and data lineage
09:20 Focus on Business Outcomes: Why AI is a talent arbitrage problem
10:16 Enterprise Metrics: How AbbVie returned 250,000 hours to employees
11:03 Hard vs. Soft KPIs: Differentiating value in automated help desk tickets
12:15 Strategic Guardrails: Defining the levels of AI governance
13:42 Formalizing Human-in-the-Loop (HITL): Three classes of evaluation
14:48 Evaluating outcomes: Active vs. Passive monitoring thresholds
15:21 Conclusion: The critical importance of AI guardrails
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