Pharma’s Digital Twins Get an AI Boost
Technology Networks speaks to Shoeb Javed to learn about the pharma potential of digital twins.
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“You now know every aspect of how you do business, who is doing it, what systems are involved, what metrics you want to measure,” Shoeb Javed, chief product officer at iGrafx, told Technology Networks.
For Javed, this is the promise of a digital twin of an organization (DTO), a virtual mirror of a company’s operations that reveals inefficiencies, pinpoints risks and continuously improves itself with the help of artificial intelligence (AI).
Speaking at Technology Networks’ Laboratory of the Future 2025 event, Javed made the case that pharmaceutical companies stand to gain the most from embracing this technology.
What is a digital twin?
In this context, a digital twin is not a 3D model of a manufacturing line or device; it is a living, evolving replica of a company’s end-to-end business processes, from drug discovery and clinical trials to regulatory submissions and manufacturing workflows. Constructed using data extracted from enterprise systems, desktop activity and historical performance, a DTO provides real-time visibility into how a company truly operates.
A DTO is more than just a static map; it is an interactive system powered by technologies such as process mining, task mining, workflow automation and predictive analytics. This infrastructure allows teams to visualize how processes run today, simulate how they might perform tomorrow and optimize them continuously based on incoming data.
“The idea is: can you gain objective visibility into how your processes actually run in reality?” Javed said. “And once you have that, you can design better processes, monitor them in production and optimize them in a cycle.”
How AI supercharges the digital twin
AI plays a foundational role in enhancing DTOs, turning them from passive dashboards into intelligent decision-making engines.
Machine learning models trained on real process data can predict outcomes of ongoing workflows, spot early signs of compliance failure or flag likely delivery delays. For example, if a crucial regulatory step is skipped early in a workflow, the DTO can alert users that the submission is likely to be rejected weeks down the line, and recommend corrective action before resources are wasted.
“A combination of process mining and AI can tell you that there might likely be a compliance issue even though it’s not explicit,” Javed explained.
AI also allows organizations to simulate what-if scenarios across operations. What happens if you reallocate trial resources? Automate a manufacturing step? Add staff to a regulatory team? By testing these changes within the DTO, companies can optimize performance without risking disruptions in the real world.
“You can simulate almost anything across any process,” said Javed, “as long as you have the right data.”
From concept to capability
To illustrate the impact of DTOs in action, Javed shared a case study from a global pharmaceutical company facing three simultaneous challenges: supply chain delays, an urgent platform migration and a new drug launch under tight deadlines.
The team deployed iGrafx’s solution – including AI-enhanced process mining and simulation – and rapidly streamlined 40 critical workflows.
“Failure to meet that target would have resulted in a daily loss of one million euros,” Javed said. With its DTO in place, the company delivered on time, avoided financial losses and gained a centralized system to support ongoing transformation.
Getting started with a DTO
For companies new to digital twins, Javed recommended starting with the basics: build a centralized process repository from standard operating procedures and existing documentation, then augment that foundation with process mining to reveal how work actually gets done.
“The first step is to build this process repository that has information about your processes,” he advised the Technology Networks audience. From there, AI can begin enhancing discovery, design and optimization, ultimately forming a dynamic, intelligent model of the organization.
In Javed’s view, the digital twin is not just another tool. It’s a framework for transforming pharma’s biggest challenges into opportunities. And AI is the engine that keeps it learning, adapting and driving results.
This content includes text that has been generated with the assistance of AI. Technology Networks’ AI policy can be found here.
