Delivering regulated AI at scale for pharma | White paper

Delivering Regulated AI at Scale for Pharmaceutical Companies is an in-depth guide for organizations ready to overcome AI delivery bottlenecks and accelerate scientific and clinical impact. This white paper shows how pharmaceutical companies like AstraZeneca enforce governance while delivering data and AI workloads at the speed of business.

Leaders in regulated pharmaceutical environments often hear that Good Practice (GxP) requirements, which demand that every data pipeline be auditable, every transformation be explainable, and every output be reproducible, force them to choose between moving quickly and maintaining compliance.

This guide demonstrates that the trade-off between speed and compliance is only a issue in manual delivery models and shows how to resolve that tension with DataOps.

Who This Guide Is For

Delivering Regulated AI at Scale for Pharmaceutical Companies is designed for:

• Pharmaceutical company data leaders and heads of data
• Analytics and data engineering teams in the pharmaceutical industry
• Platform and architecture teams at pharmaceutical organizations
• Pharmaceutical product managers working with data governed by GxP

Why is AI such a challenge for the pharmaceutical industry?

Regulated pharmaceutical environments are bound by Good Practice (GxP) requirements, which raise the standards for data management. Under GxP:

• Every data pipeline must be auditable
• Every transformation must be explainable
• Every output must be reproducible

Under a traditional approach to data delivery, manual GxP compliance becomes a bottleneck. Cycles take weeks or months, and innovation suffers.

Pharmaceutical organizations can reduce that friction by shifting to an automated DataOps operating model that embeds GxP into governed data pipelines, increasing speed without increasing risk.

What You’ll Learn in Delivering Regulated AI at Scale for Pharmaceutical Companies

This white paper is designed to give pharmaceutical industry data leaders all the information you need to understand DataOps automation and how it ensures GxP without compromising speed. Inside, you’ll learn:

• The top reasons that AI initiatives in pharma consistently fail, despite advances in tooling and cloud infrastructure
• Why seeing regulated data delivery as a choice of speed vs compliance is misguided, and how to escape this framing
• How DataOps automation applies the proven DevOps operating model to fix regulated AI delivery at scale
• Why the automation shift is about how organizations execute and manage regulated data delivery, not just tooling
• An industry case study from AstraZeneca demonstrating how shifting to an automated DataOps delivery model enables regulatory rigor at speed
• 5 steps pharma leaders who aren’t ready to replatform can take to start introducing automation

The guide provides key insights into regulated data delivery, a real-world case study, and actionable guidance to help you escape the false speed-vs-compliance constraints of GxP compliance and help your organization achieve its AI goals.

Download the Delivering Regulated AI at Scale for Pharmaceutical Companies whitepaper now

Get your copy of Delivering Regulated AI at Scale for Pharmaceutical Companies to learn how pharmaceutical organizations like AstraZeneca are shifting to automated DataOps to move from slow experimentation to regulated, production-scale AI.

Fill out the form at the top of the page to download your copy now!