These tools enable researchers to identify potential issues far earlier than they could with conventional monitoring methods, ensuring faster interventions and more reliable trial outcomes.
TCS has introduced an AI-enhanced version of its TCS ADD Risk-Based Quality Management (RBQM) platform, offering drugmakers and research organisations clearer, real-time visibility into clinical trials. The upgrade is designed to detect risks sooner, strengthen data quality, and streamline the management of increasingly complex study environments.
The upgraded platform adds four AI-driven modules for risk assessment, quality tolerance limits, trial analytics, and subject-level data monitoring. These capabilities enable research teams to spot emerging issues far earlier than they could with traditional monitoring approaches.
TCS notes that these modules are among the few worldwide that offer full interoperability and can be tailored to different clinical trial designs. This flexibility helps sponsors reduce deployment time and accelerate study setup.
In today’s rapidly changing clinical research landscape, traditional quality management methods are no longer enough,” said Rachna Malik, Global Head of TCS ADD. She noted that the enhanced platform enables faster, data-driven decision-making and can help accelerate the delivery of new therapies to patients.
Industry trends reinforce TCS’s move toward AI-led oversight. AI adoption in India’s healthcare GCCs has climbed from 65% in 2019 to 86% in 2024, according to Zinnov managing partner Karthik Padmanabhan. He noted that AI has become essential for enhancing patient recruitment, identifying risks earlier, and supporting regulatory compliance.
The update arrives at a time when the life sciences sector is leaning more heavily on AI and analytics to meet tougher regulations and manage the complexities of decentralised and adaptive trials. TCS says the platform aligns with global standards, including ICH E6(R2) and the forthcoming E6(R3), and embeds Quality by Design principles from study planning through execution.
TCS reports that the platform has already been deployed in over 1,300 studies across 32,000 sites, signalling that AI-driven oversight is rapidly becoming a standard in modern clinical research.
