As with any digital transformation, the true impact of AI can only be felt when it is embedded across the entire clinical lifecycle rather than confined to isolated pilots.
AI has evolved from a supportive tool to a central engine accelerating every phase of clinical research, serving as a trial performance copilot, regulatory intelligence engine, and even a submission content writer.
The promise is significant, with AI projected to unlock $60 to $110 billion in annual value across the pharma value chain. However, despite the promise, many organizations still struggle to bridge the gap between AI’s capabilities and their operational readiness.
Edwin Ng, Senior Vice President and General Manager, Asia Pacific, Medidata, if of the view that pharmaceutical leaders need to adopt an end-to-end approach that connects data, patient experience and operations to unlock AI’s full potential. We find out more in this interview…
Do you think it’s time for the pharmaceutical industry to move beyond AI pilots?
Ng: The time has come for the pharmaceutical industry to move decisively beyond AI pilots and toward integrated, scalable solutions. The potential value is no longer theoretical with AI projected to unlock between $60 to $110 billion in annual value across the pharma value chain and we are already seeing real-world impact in areas like data cleaning, edit checks, and real-time patient content translation.
These applications are accelerating trial timelines and improving data quality. But the real shift will come when AI is treated not as an experiment, but as a core, functional capability across the organization. Trials are becoming more adaptive and patient-centric, with AI enabling study designs that evolve in real time based on patient input. This shift allows for smarter decisions and improved patient experiences.
At Medidata, we believe that embedding AI across the clinical trial lifecycle is key to driving meaningful transformation in drug development. Our “AI Everywhere” strategy reflects this transformation from trial design and site selection to patient modeling and efficacy analysis. This approach has already delivered tangible results, such as reducing study startup time by up to 75% and study build time by as much as 80%.
In collaboration with AbbVie, we also used AI to analyze real-world data in platinum-sensitive ovarian cancer patients, helping establish critical benchmarks for future therapies and uncovering patient subgroups with better outcomes. These are just some of the ways AI is actively advancing the future of drug development.
The broader industry is also recognizing this shift. According to Deloitte’s 2025 Life Sciences Outlook, nearly 60% of pharma executives plan to increase generative AI investments across the value chain. This marks a clear move from experimentation to implementation. Moving beyond pilots is not just about deploying more AI but making AI a core driver of innovation, so we can bring safer, faster, and more personalized treatments to patients worldwide.
What does it entail to embrace an end-to-end AI transformation mindset?
Ng: Embracing an end-to-end AI transformation mindset in pharma requires a fundamental shift not just in technology adoption, but in how organizations think, operate, and deliver value. It means moving from using AI as a standalone tool to embedding it as a strategic capability across the entire value chain.
This kind of transformation starts with leadership alignment around long-term AI investment. Equally important is building the right foundation. To scale AI effectively, organizations need a robust tech stack that meets security, performance, and latency needs.
With many AI applications becoming commoditized, organizations also need to strike a thoughtful balance between building in-house and leveraging external partners. A clear financial governance model, such as a FinOps framework, could help manage costs, track return on investments (ROI), and allocate resources efficiently.
Ultimately, adopting this mindset means reimagining every step of the pharma value chain. It is about asking hard questions regarding processes, data, technologies, talent, and change management. Organizations that can make this shift successfully will be the ones that unlock AI’s full potential, transforming how therapies are discovered and developed.
Why is Asia Pacific well-positioned to be the global testbed for AI-enabled, decentralized, patient-centric trials?
Ng: Home to over half of the world’s population, Asia Pacific (APAC) has rapidly emerged as a global hub for clinical research. Between 2013 and 2022, it recorded the fastest growth in early-stage clinical trials globally, expanding at a rate 12 times higher than the US and four times higher than Europe.
This growth is fueled by the region’s strong R&D capabilities, deep talent pools, world-class healthcare infrastructure, and increasingly robust regulatory frameworks. What’s more, according to a Deep Pharma Intelligence report, APAC is also at the forefront of AI integration in clinical research, with approximately 700 companies across the region actively applying AI technologies across the drug development lifecycle.
These capabilities are helping sponsors streamline trial operations and reduce costs. Looking ahead, the region is projected to lead the global AI-driven drug discovery market with a 45% CAGR, fueled by increasing healthcare investment, growing awareness of AI’s potential, and proactive government support across key markets such as China, Japan, and India.
The integration of AI and digital health technologies is also ushering in a new era of decentralized and inclusive trial models. By removing traditional barriers to participation, such as location or mobility constraints, these tools are making it easier for diverse patient populations to engage in clinical research more easily.
This is particularly transformative in therapeutic areas like oncology, where decentralization allows seriously ill patients to reduce in-person visits and participate from the comfort of their homes. This transformation towards more accessible and patient-centric trials is not only enhancing the quality and diversity of research data but also accelerating the pace of medical advancements across the region.
