Dennis Salotti, executive director and head of clinical outsourcing and innovation at Jazz Pharmaceuticals, joins Srivatsan Nagaraja to talk about what AI really looks like in day-to-inside a drug company. He explains how large language models and agentic tools can streamline contracting, budgeting, and risk management—mundane but high-impact work that speeds studies and improves trial-site experience. Salotti shares how to design narrow, high‑ROI pilots, build AI fluency, avoid “AI slop,” and turn AI into a thinking partner rather than a shortcut, stacking small wins that remove grunt work, capture lessons learned, and elevate people from doers to critical thinkers.

Tris Pharma Chief Commercial Officer Manesh Naidu sits down with Nagaraja Srivatsan to discuss how the small but growing specialty pharma company is pursuing a deliberately incremental AI strategy, focusing on its biggest pain points rather than chasing grand, enterprise-wide transformations. He explains how the team is using off-the-shelf and vendor tools where possible, rigorously managing data and compliance risk, and letting quick wins in discrete use cases pave the way for broader change. Naidu discusses what practical AI adoption looks like for Tris, how the company is applying the technology to high-value commercial challenges, and how it is reinventing sales training through virtual physician simulations.

Shweta Maniar, global strategy and market leader for life sciences at Google Cloud, sits down with Nagaraja Srivatsan to unpack what it takes to turn AI pilots into durable, enterprise-wide impact. A former biotech executive who now helps biopharma leaders modernize their data and AI strategy, Shweta argues that success starts with fixing fragmented data foundations and reshaping culture—not just deploying the latest model. She explains why AI should be treated as a strategic upgrade to de-risk science rather than a narrow tool for squeezing efficiency, why the hardest problems are less about model performance and more about data and organizational behavior, and how emerging multi-agent systems could compress timelines from discovery to patient access. You can download The ROI of AI in Healthcare and Life Sciences report referenced in the discussion here: https://cloud.google.com/resources/content/roi-of-ai-healthcare-life-sciences