TCS has rolled out new AI-powered upgrades to its TCS ADD risk-based quality management (RBQM) platform designed to help pharmaceutical companies and research institutions monitor clinical trials more accurately and in real-time. The platform aims to help teams detect risks early, improve data quality, and manage increasingly complex trial settings.
The new version introduces four AI-driven modules focused on risk assessment, quality tolerance limits, trial analysis, and subject-level data monitoring. When used together, these tools can help researchers detect problems much faster than traditional monitoring methods.
According to TCS, these modules are fully interoperable and are one of the few modules in the world that can be customized to suit a variety of test designs. This feature reduces deployment time for sponsors.
“In today’s rapidly evolving clinical research environment, traditional approaches to quality control are no longer sufficient,” said Rachna Malik, Global Head of TCS ADD. He said the upgraded platform will support faster, data-driven decision-making and bring new treatments to patients more quickly.
Industry trends support TCS’ push towards AI-driven surveillance. Zinnov Managing Partner Karthik Padmanabhan told AIM that AI adoption rates in India’s healthcare GCC have risen sharply from 65% in 2019 to 86% in 2024, with AI tools now central to improving patient recruitment, monitoring risks, and ensuring regulatory compliance.
This update comes as the life sciences industry increasingly relies on AI and analytics to meet the challenges of increasingly stringent regulations and distributed, adaptive testing. TCS notes that the platform is compliant with international guidelines ICH E6(R2) and the upcoming E6(R3) and incorporates design-by-quality principles from research inception to execution.
According to TCS, the platform has been used in more than 1,300 studies across 32,000 sites, showing that AI-powered surveillance is rapidly becoming a standard part of modern clinical research.
