AWS re:Invent 2025 - Next-Generation Data Management — Insights at Scale with Agentic AI in Pharma

Summary of AWS re:Invent 2025 - Next-Generation Data Management — Insights at Scale with Agentic AI in Pharma

Key Themes from Pharma CIOs

• Pharma CIOs are shifting their focus from experimentation to driving real transformation and value from Agentic AI.
• 9 out of 10 CIOs want to rethink and reimagine their processes to be optimized for Agentic AI, rather than just automating existing workflows.
• CIOs are demanding the pace of digital and AI innovation to grow and scale to enable this transformation.

Two Paradigms of Data Management for Agentic AI

1. Data for AI:

   • Ensuring high-quality, accurate data to power Agentic AI models and insights.
   • Going beyond just table/column metadata to create a comprehensive "metadata lake" that captures lineage, business rules, and domain-specific context.
   • Requires deep business involvement to codify the right context and nuances.
   • Can drive insight accuracy from 70% to 98% by implementing this metadata-centric approach.

2. AI for Data Management:

   • Using Agentic AI to optimize the data engineering lifecycle, from requirements to deployment.
   • Leveraging Agentic AI to automate data governance, quality, and metadata management - creating a "self-healing" data ecosystem.
   • Enables greater efficiency (up to 40%) and sustainability in data management operations.

Key Use Cases

1. Automating Analytic Workflows:

   • Codifying human analytic workflows and using Agentic AI agents to execute them, reducing manual effort by 40%.
   • Integrating Agentic AI into the entire analytics value chain, from data preparation to insight generation.

2. Intelligent Document Generation:

   • Using generative AI to power knowledge hubs and automate the creation of clinical, commercial, and supply chain documents.
   • Ensures compliance and validation of document generation processes.

3. Optimizing the Software Development Lifecycle:

   • Leveraging Agentic AI for code generation, testing, and other SDLC tasks to achieve up to 40% efficiency gains.
   • Requires careful consideration of infrastructure and cost management to scale these Agentic AI transformations.

Key Takeaways

1. Focus on business process transformation, not just automation - rethink processes to be optimized for Agentic AI.
2. Context is critical - invest in comprehensive metadata management and deep business involvement to enable accurate Agentic AI insights.
3. Adopt a cross-functional, collaborative approach - IT, digital, and business teams must work together to drive successful Agentic AI transformations.
4. Ensure robust infrastructure and cost management - Agentic AI can quickly scale infrastructure costs if not properly managed.
5. Start with a clear vision of the value and ROI - consider both efficiency gains and new value creation opportunities.

Specific Results Mentioned

• Increased insight accuracy from 70% to 98% by implementing a comprehensive metadata management approach.
• Up to 40% efficiency gains in analytic workflows and the software development lifecycle by automating with Agentic AI.
• Significant time and cost savings in clinical, commercial, and supply chain document generation through the use of generative AI.