Whitepaper

Unifying fragmented data with agentic workflows in 2026

When regulators ask how a decision was made, can your infrastructure answer? Discover how pharma enterprises are building the data foundation that makes agentic AI explainable, trustworthy and ready to scale across the organization.

Put your AI under a microscope before your regulator does

It’s time to inspect what’s underneath. When data is fragmented across systems that were never designed to work together, it makes insights slow to produce, difficult to validate, and hard to defend when it matters most. 

Agentic AI workflows built on a solid knowledge foundation change this. It harmonizes data across disparate sources, retains evidence at every step, and produces outputs that are repeatable and traceable by design. 

Since 2013, Digital Science has been building AI-ready infrastructure for pharma and life sciences companies. This whitepaper sets out what that looks like in practice.

What you’ll learn in this whitepaper:

• Why general-purpose AI fails in high-stakes environments and the specific architectural gap that causes outputs to be unverifiable, unrepeatable, and impossible to defend in a regulated context
• What agentic AI actually changes for R&D: how a mid-size oncology biotech cut a three-to-four day target identification review to hours, with every assertion backed by a traceable evidence chain
• Why traceability is architecture, not afterthought: how agentic workflows retain links to underlying source material at every step, so decision-makers can review the reasoning, not just the conclusion
• How governance makes AI programmes sustainable: why access controls, audit trails, and data flow clarity need to be built in from the start, and how this investment compounds over time
• Where to start and how to scale: a practical framework for scoping your first workflows, avoiding the most common implementation failure modes, and building capability that grows with the enterprise