End-to-end adoption of AI expedites drug development timelines, say leaders at BioAsia conference in Hyderabad

Hyderabad: Life sciences and medtech global capability centres (GCCs) in India are shifting from cost-focused delivery units to AI-powered innovation hubs, with companies deploying artificial intelligence across research, clinical, commercial and manufacturing workflows to speed development and improve patient impact.At BioAsia 2026, leaders from big pharma described how GCCs are increasingly being tasked with end-to-end ownership of digital and R&D programmes.
Anton Groom, chief AI officer at MSD, said AI is being used across the company to accelerate molecule discovery, compress clinical documentation timelines and unlock value from decades of research data.Amgen's Som Chattopadhyay, SVP, global business solutions and national executive, said AI is acting as a talent multiplier, helping teams work more effectively with unstructured data and lifting productivity.Novartis chief marketing and customer experience officer Gail Horwood said Hyderabad-based teams are building AI-driven content engines and real-time insights platforms to support patient and physician engagement in the US. Syed Naveed, executive officer and CTO at Olympus, pointed to a broader shift toward "true innovation," with AI-enabled clinical and product development emerging from Hyderabad's ecosystem.In a separate discussion on GCCs and end-patient impact, industry leaders said these centres are being positioned to address headwinds including ageing populations, shrinking clinical workforces, pricing pressure, tighter regulation and supply-chain volatility. They said GCC-led programmes are improving trial readiness, accelerating innovation cycles and enabling connected care models.A report titled "Making it matter: How GCCs transform capability into end patient impact," developed by KPMG in India and UnearthIQ, said India's pharma and life sciences GCCs are delivering measurable gains across the drug development value chain.It said end-to-end adoption of AI, automation and advanced analytics is compressing drug development timelines from 10 to 15 years to nine to 13 years, while R&D-to-launch costs have declined from 20–30% to 15–25%.The report added that GCCs are cutting early-stage timelines by five to six years through faster target identification, protein modelling and compound screening, and shortening clinical development cycles by four to six years via AI-driven patient recruitment, trial design and real-time analytics.