> **来源：[研报客](https://pc.yanbaoke.cn)** ```markdown # Summary of "Industries in the Intelligent Age: Strategic Choices in the Age of AI - Shaping the Future of Life Sciences" ## Core Content This report explores how artificial intelligence (AI) and digital transformation are redefining the life sciences industry, particularly in biopharma and medtech. It outlines the structural changes in research, development, market access, and distribution, emphasizing the need for strategic rethinking in an AI-driven era. ## Main Viewpoints - **AI is reshaping the entire value chain**: AI and digital tools are not just optimizing existing processes but fundamentally reconfiguring how value is created and delivered in life sciences. - **Competitive advantage is evolving**: Traditional R&D models and commercialization strategies are being replaced by new, AI-integrated approaches that prioritize speed, insight, and strategic positioning. - **Public-private collaboration is essential**: The success of AI-driven transformation in life sciences depends on alignment between public and private sectors, particularly in regulation, reimbursement, and data governance. - **Strategic choices are becoming more explicit**: Companies must choose how to integrate AI into their operations, whether as AI-supported science champions, asset integrators, or development engines. ## Key Information ### 1. AI and Digital Transformation in Life Sciences - AI is already deeply embedded in the life sciences value chain, driving significant improvements in speed, cost, reliability, and scalability. - The industry has moved from isolated digital applications to AI as core infrastructure, integrated into processes, data flows, and decision systems. - AI is not replacing human judgment but changing how and where it is applied, with a shift from traditional clinical phases to continuous learning systems that combine clinical trial data with real-world evidence. - The performance baseline of the industry has been raised by digital and AI capabilities, redefining what it means to be a best-in-class organization. ### 2. R&D Ecosystem and Competitive Advantage - **AI removes scarcity in research**: It enables rapid hypothesis generation, in-silico experimentation, and automated iterations, increasing the number and quality of candidate molecules. - **R&D is becoming more data-driven**: AI supports deeper biological insights, translational understanding, and experimental learning, allowing for earlier uncertainty reduction. - **Three strategic approaches emerge**: - **AI-supported science champion**: Focuses on deep biological understanding and uses AI to enhance scientific reasoning and insight. - **Asset integrator**: Builds on external innovation by systematically sourcing, selecting, and scaling externally generated assets. - **Development engine**: Competes through superior execution in clinical development, evidence generation, and real-world translation. ### 3. Market Access and Distribution - AI and digital interfaces are redefining how demand is formed and how products reach patients, with multiple access pathways emerging (provider-led, direct-to-patient, and new intermediary models). - These models require distinct capabilities, operating models, and economic logic, and companies must align their strategies accordingly. - The industry is witnessing an exponential increase in AI-native discovery output, with many AI-discovered molecules entering clinical trials. ### 4. Implications for Public-Private Collaboration - Regulatory frameworks, reimbursement models, and data governance will shape which AI-driven approaches can scale and where capabilities concentrate. - The next phase of this work will integrate the public sector perspective to better understand the conditions needed for responsible, scalable, and equitable transformation. - A shared vision and multistakeholder alignment are critical to translating technological disruption into sustainable value. ### 5. Industry Readiness and Challenges - Readiness for AI integration remains uneven, with only 34% of medtech and 29% of biopharma players considered "AI-scalers" or "future-built" according to BCG's 2025 study. - Companies must balance exploratory research with portfolio discipline and avoid hollowing out core scientific expertise for short-term gains. - The industry must invest in re-skilling, data foundations, and organizational redesign to fully leverage AI's potential. ## Conclusion The transformation in life sciences driven by AI is not incremental. It requires a shift from optimizing existing models to building fundamentally new ones. Companies must align their capabilities, operating models, and investment with a clear strategic vision to thrive in the intelligent age. The future of life sciences depends on embracing AI as a core enabler of innovation, trust, and value creation, while fostering collaboration across sectors and stakeholders. ```