AI readiness in pharma is the focus of a new opinion piece by Narasimha Kumar published in Bio‑IT World. The article, entitled “AI readiness in pharma – Getting the foundation right,” examines how data fragmentation, complex regulations, and limited interoperability are slowing AI adoption—and what it takes to build trusted, cross‑border evidence ecosystems.
AI readiness in pharma depends on the ability to integrate clinical, genomics, and real‑world data in a secure and compliant way. As data volumes grow and studies become more global, ensuring interoperability and governance becomes essential to generate reliable evidence and accelerate drug development.
Many years ago, when AI was seen more in science fiction than in the real world, we were asked a seemingly simple question in an innovation workshop – why is it that our bank accounts and credit cards can be used globally, but we can’t do the same with our health information? The participants spoke about the challenges of interoperability, and the need for patient-centric harmonization.
Recently, I was in conversation with a pharma executive, who wanted to know how quickly AI can help accelerate evidence generation.“Before we talk about AI,” I said, “can we bring together your clinical, genomics, and real-world data? Can it legally and securely be accessed across borders?”
In “Building a reusable evidence foundation for confident decision-making,” published in The Evidence Base, Kumar explains how reusable data foundations are becoming essential for teams to generate trusted, decision-ready evidence across the product lifecycle.
“AI in drug development is not a data problem – it’s an evidence problem“, published in MedCity News, Kumar explores how the challenge for organizations is no longer collecting data, but preserving the context, meaning, and relationships that transform data into usable evidence.
In “How data from medical practice can inform care,” published in MedNous, Kumar explores how real‑world data challenges traditional views of standard of care and enables more accurate, data‑driven decisions across trial design and evidence generation.