The preceding decade was marked by rapid growth and robust policy backing; however, the landscape has fundamentally evolved. Research and development expenses persist in escalating, competition has intensified across nearly all therapeutic sectors, commercial pressures are increasing, and international expectations for Chinese innovation have reached unprecedented levels. The conventional approach—prioritizing rapid growth, substantial investment, and dependence on policy support—no longer assures enduring success.

The industry is transitioning from a policy-focused to an efficiency-focused era, with artificial intelligence at its core. AI has moved from being a peripheral idea to a fundamental operational tool, transforming medicine discovery, clinical trial processes, manufacturing operations, and company-patient engagement. More critically, AI is beginning to reshape the industry’s competitive landscape—impacting how companies differentiate themselves, collaborate, and create value. This change is increasingly evident in strategic deals, as capital becomes more selective and valuations stabilize. Pharmaceutical firms are now increasingly engaging in M&A, licensing, and co-development not only to expand their pipelines but also to acquire essential AI capabilities to stay competitive. AI plays a key role in influencing acquisitions, licensing, and funding decisions, often acting as the decisive factor.

AI has a significant impact on drug discovery, transforming a traditionally lengthy, trial-and-error process—spanning three to five years—into a more efficient, data-driven approach. AI accelerates timelines by generating new molecules, predicting their traits, and filtering out weak candidates before lab testing. By 2026, China’s AI-powered discovery platforms are expected to be highly advanced. Generative models are now commonplace, and Chinese firms are creating first-in-class and innovative molecules that were unimaginable only a few years ago. This progress is influencing industry partnerships: multinational companies are increasingly collaborating with Chinese AI platforms, shifting from basic service agreements to milestone-based deals, shared-risk models, and long-term royalty agreements. Domestic firms are also advancing rapidly—some acquiring AI startups, others forming joint ventures to develop in-house AI engines. Licensing of AI-designed molecules is forecasted to grow, especially in oncology, immunology, and metabolic diseases. AI platforms are becoming strategic targets for acquisition as companies seek to embed intelligence within their R&D processes.

Clinical development is also experiencing a transformation. China’s clinical trial ecosystem has expanded rapidly, but ongoing challenges persist: slow patient recruitment, inconsistent data quality, and significant regional disparities. AI is playing a key role in addressing these issues. By analyzing electronic medical records, imaging data, and genomic information, AI can identify eligible patients with much greater accuracy. It also helps optimize trial protocols, predict safety risks, and automate data cleaning. As regulators become more comfortable with real-world data, China’s extensive clinical datasets are gaining strategic importance. These advances are also altering deal activity, with pharmaceutical companies forming deeper partnerships with AI-enabled CROs or acquiring these capabilities directly. 2026 is expected to be a strong year for M&A in this sector, especially for companies focused on patient recruitment, real-world evidence, and decentralized trial operations. Multinational companies are increasingly relying on Chinese AI clinical platforms to enhance development efficiency across Asia.

Looking ahead, AI will continue to develop from a productivity booster into a core capability that provides a competitive edge. Companies that effectively incorporate AI throughout discovery, development, manufacturing, and commercialization will outperform their peers in speed, cost efficiency, and innovation quality. In this environment, M&A, licensing, and co-development will take on an increasingly strategic role—speeding up capability building, facilitating technology transfer, and helping companies scale AI-driven innovation more quickly.