> **来源：[研报客](https://pc.yanbaoke.cn)** # KPMG Global Tech Report 2026: Life Sciences Summary ## Core Content Overview The **KPMG Global Tech Report 2026: Life Sciences** provides insights into the current state of technology adoption, investment, and value realization across the Life Sciences sector. It is based on a survey of 124 technology function leaders from large and mid-sized pharmaceutical, biotechnology, medical device companies, as well as product distributors and contract research organizations. ## Main Findings ### **Technology Adoption and Maturity** - **AI Adoption**: 87% of organizations have integrated AI agents into workflows, products, and services. 75% trust AI outputs for strategic and operational decisions. - **Adoption by Technology Category**: - Core technologies like AI, cybersecurity, and data analytics are widely adopted. - Immersive and emerging technologies such as VR/AR/XR, Web3, and digital twins are progressing more slowly. - **Maturity Levels**: - Most organizations are in the **managed** or **optimized** stages of maturity. - **Enterprise data management**, **cloud infrastructure**, and **technology sourcing and vendor management** show low maturity, which is a key constraint on scaling advanced capabilities. - **Maturity Gaps**: - Low maturity in data management and cloud infrastructure limits the ability to scale technologies like AI, digital twins, and quantum computing. - These gaps also increase costs and reduce competitiveness due to inefficient management and lack of centralized control. ### **Investment Trends** - **Investment Allocation**: 97% of Life Sciences organizations allocate less than 1% of annual revenue to digital technology. - **Investment Priorities**: The industry is cautious and conservative in investment behavior, emphasizing stability and compliance over rapid expansion. - **Investment Strategy**: - A **balanced approach** is common, with investments focused on maintenance and incremental upgrades. - Top priorities for the next 12 months include **cybersecurity**, **AI and automation**, and **data and analytics**. ### **Value Realization** - **Current Value**: 48% of organizations are achieving significant value from their technology investments. - **Value Realization Challenges**: - 93% of digital initiatives generate less than 1% of annual revenue. - The **scale/value capture gap** is a critical issue, with most benefits focused on **operational efficiency** rather than **transformational growth**. - **ROI by Revenue Range**: - 45% of organizations report ROI between 90-150%. - 14% report ROI over 400%. - The majority (62%) derive value from **foundational platforms**, but the impact tends to plateau. ### **Technology Function Maturity** - **Maturity by Function**: - **Cybersecurity**: 50% managed. - **IT Service Management**: 42% managed. - **Enterprise Data Management**: 29% optimized. - **Cloud Infrastructure**: 39% managed. - **Maturity Stages**: - **Developing or Initial/Ad Hoc**: Processes are informal and reactive. - **Defined**: Standardized processes across teams. - **Managed**: Actively monitored and measured. - **Optimized**: Continuous improvement and best practices. ## Key Takeaways - The Life Sciences sector is in the **early stages of digital maturity**, with most benefits focused on **operational efficiency**. - A **scale/value capture gap** exists, where significant value is realized but not scaled to enterprise-wide impact. - **AI adoption** is widespread but limited in its ability to scale due to governance, funding, and platform constraints. - **Cloud/XaaS modernization** is seen as a prerequisite for broader digital transformation and the successful scaling of AI and other emerging technologies. ## Recommendations 1. **Fill Foundational Gaps**: Evaluate and address weaknesses in data management, cloud infrastructure, and vendor management. 2. **Cloud/XaaS Modernization**: Treat cloud migration and optimization as a **prerequisite program** for transformation. 3. **Optimize Maturity**: Transition core platforms from a **project delivery model** to a **product operating model**, assigning them to dedicated product teams. 4. **Buy Platform, Build Differentiation**: Use a **hybrid capability strategy**, purchasing standard platforms and building unique differentiating assets. 5. **Hybrid Decision-Making**: Combine **federated decisions** with **enterprise control** to balance speed and scale. 6. **Establish Scaling Mandates**: Create a clear path to production for AI and digital pilots, including platform, funding, owner, and KPIs. 7. **Rebase Digital Investment**: Reallocate resources based on **ROI evidence**, focusing on initiatives that deliver measurable value. ## Investment and Governance - A **federated governance model** is common, with **centralized control** in compliance-critical areas and **democratized ownership** in operational tasks. - Organizations are moving away from the traditional “build or buy” dilemma toward **hybrid strategies** that include **buying or borrowing** proven solutions and enhancing them with AI. ## Future Outlook - AI is expected to become a **core driver of transformation**, moving from experimental use to **embedded capabilities**. - Over the next five years, AI will be integrated into **strategic domains** such as M&A, pricing, and regulatory affairs. - The **future of healthcare** lies in **data-driven insights** and **agile technologies** that enable **personalized treatments** and **collaborative innovation**. ## Conclusion While the Life Sciences sector has made progress in digital transformation, it still faces significant challenges in scaling and realizing value from emerging technologies. Addressing foundational gaps and evolving governance and operating models are critical to unlocking greater impact and moving toward a more **patient-focused**, **innovative**, and **collaborative future**.