Life sciences organizations are facing growing challenges in managing increasingly complex enterprise data environments across EDW and MDM ecosystems. Traditional data stewardship models often struggle to keep pace with rising data volumes, fragmented systems, and the need for faster, more reliable decision-making.
In this session, we explored how agentic AI is transforming data stewardship from a reactive support function into a proactive, intelligence-driven capability. The discussion covered how organizations can leverage AI to monitor data pipelines, prioritize high-impact issues, automate stewardship workflows, and improve visibility across enterprise data operations. We also examined practical approaches to scaling stewardship while maintaining governance, control, and data quality standards across life sciences organizations.