HONG KONG - Tencent Healthcare's president, Alex Ng, said smaller biotechnology companies are quicker to adopt artificial intelligence tools than their larger pharmaceutical counterparts, as many firms look to AI to raise efficiency and cut costs in research and development.
Ng attributed the faster uptake among small biotechs to their organizational scale and operational pressures. "When you have less people, when you need to do more, you tend to figure out more efficient ways of doing things," he said. "AI is definitely something that they latch onto very quickly," Ng added.
His remarks underscore a pattern in which smaller teams, constrained by limited headcount and resources, pursue technological solutions to accelerate work and reduce manual burden. For those companies, AI-based modeling tools and automated laboratory processes can offer outsized productivity gains relative to their size.
By contrast, Ng noted that very large pharmaceutical companies - with elaborate organisations and specified workflows - can encounter barriers when trying to integrate AI. "For very big pharmaceutical companies, however, with elaborate organisation and specified workflows, just adding an AI will sometimes not be successful," he said. The implication is that scale and complexity can blunt the immediate benefits of drop-in AI tools unless processes and structures are adapted.
Industry forecasts cited in the discussion suggest that using machine learning to optimise target discovery, design molecules and streamline clinical trial planning could halve early-stage development timelines and costs within the next three to five years. That projection frames AI as a potentially transformative tool for research and development timelines, though it is presented as a forecast rather than a guaranteed outcome.
Ng also commented on shifting attitudes toward the technology. "I think the culture and the environment and the discussion has changed so much that I think a lot of people are a lot more positive," he said, reflecting greater openness across parts of the sector as AI capabilities improve.
Context and implications
- Smaller biotechnology firms appear more nimble in deploying AI to boost productivity and compress R&D cycles.
- Large pharmaceutical firms may need organisational change and workflow adaptation to realise benefits from AI deployment.
- Industry forecasts point to substantial potential reductions in early-stage development time and cost over a three- to five-year horizon, contingent on successful implementation.