AWS re:Invent 2025 - Next-Generation Data Management — Insights at Scale with Agentic AI in Pharma
Source: Dev.to
Overview
AWS re:Invent 2025 – Next‑Generation Data Management — Insights at Scale with Agentic AI in Pharma
In this video, a ZS representative discusses agentic AI transformation in life‑sciences data management. The speaker emphasizes that 9 out of 10 CIOs are shifting from proof‑of‑concepts to full‑scale implementation, focusing on value creation rather than just automation.
Core Paradigms
- AI for data – achieving up to 40 % efficiency gains in data engineering.
- Data for AI – creating comprehensive metadata lakes to improve model accuracy from 70 % to 98 %.
Key Use Cases
- Automated analytics workflows
- Clinical document generation
- Software development lifecycle optimization (up to 75 % efficiency gains in testing)
Success Factors
- Rethinking processes before automation
- Robust infrastructure planning
- Cross‑functional collaboration between business and IT teams
- Rich business context as the differentiator in agentic AI implementations
This article is auto‑generated while preserving the original presentation content as much as possible. Typos or inaccuracies may be present.
Main Part
Life Sciences CIOs Shift from Experimentation to Transformation
“Their number‑one priority is to see value from agentic AI. Historically we’ve run many pilots and POCs; now the question is how we enable transformation and deliver insight at scale.”
CIOs are faced with two paths:
- Quick wins – automate existing processes as‑is.
- Fundamental redesign – re‑imagine processes so they can be truly optimized by agents.
A recent ZS survey of life‑sciences CIOs shows 9 out of 10 want the pace of digital, AI, and tech innovation to grow and scale. This shift demands a focus on end‑value, people, and process changes rather than technology for its own sake.
Core Paradigms in Data Management
ZS defines two complementary paradigms:
| Paradigm | Goal | Example Benefits |
|---|---|---|
| Data for AI | Prepare high‑quality, well‑governed data to feed generative models | Improves accuracy from ~70 % to ~98 % |
| AI for Data | Apply AI to streamline data engineering and operations | Up to 40 % efficiency gains in engineering; sustained data quality, governance, and security |
Data for AI
Generative AI models often deliver sub‑optimal results when trained on noisy or incomplete data. By building metadata lakes and enforcing rigorous data governance, organizations can raise model reliability to near‑enterprise standards.
AI for Data
AI can be leveraged in two stages:
- AI for data engineering – Optimizing the software development lifecycle (requirements → deployment) to achieve up to 40 % efficiency gains.
- AI for data operations – Automating data quality checks, governance policies, access management, and security to maintain the data product over time.
Sustaining Metadata Governance
Metadata curation is traditionally labor‑intensive. By applying AI to both create and maintain metadata, organizations can reduce manual effort and keep data assets discoverable and trustworthy.
Three Areas of Transformation
- Reimagining data consumption – With curated data, agentic AI enables new human‑agent interaction models, allowing insights to be consumed in more intuitive ways.
- Transforming analytic workflows – Moving beyond passive receipt of results to proactive, AI‑driven decision support.
- Embedding AI into the operating model – Ensuring that AI‑generated data products are governed, secure, and continuously improved.
“How do I transform my analytic workflows so that I no longer just have to receive an in…”
(The transcript ends abruptly here.)