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GSMA：2026规模化AI影响力：基础、商业模式与影响路径（中译版）（57页）.pdf

上传人： 1****1 编号：1192347 2026-04-17 57页 6.77MB

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1、AI for Impact at ScaleFoundations,Business Models and Impact PathwaysAI for Impact at Scale:Foundations,Business Models and Impact Pathways2The GSMA is a global organisation unifying the mobile ecosystem to discover,develop and deliver innovation foundational to positive business environments and so

2、cietal change.Our vision is to unlock the full power of connectivity so that people,industry and society thrive.Representing mobile operators and organisations across the mobile ecosystem and adjacent industries,the GSMA delivers for its members across three broad pillars:Connectivity for Good,Indus

3、try Services and Solutions,and Outreach.This activity includes advancing policy,tackling todays biggest societal challenges,underpinning the technology and interoperability that make mobile work,and providing the worlds largest platform to convene the mobile ecosystem at the MWC and M360 series of e

4、vents.We invite you to find out more at GSMA EmergingTech ProgrammeThe GSMA EmergingTech programme accelerates impact and climate action by fostering the adoption of AI and emerging technologies in low-and middle-income countries(LMICs)by working with public,private and third sector innovators to de

5、velop scalable and sustainable solutions that have inclusive and responsible AI at the core.The Emerging Tech programme works closely with the GSMA AI for Impact initiative to drive real-world,impact-focused implementation with telcos in LMICs.To get in touch with the Emerging Tech team,please email

6、:Author:Ibrahim Sajid(GSMA Mobile for Development)Acknowledgements:We would like to thank the many individuals and organisations that contributed to this research.This includes EY,World Food Programme,World Bank,UNDP,Global Partnership for Sustainable Development Data(GPSDD),Tony Blair Institute,Gen

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1. **核心目标**：GSMA通过AI与新兴技术推动低收入和中等收入国家（LMICs）在健康、农业、气候等领域的规模化影响，强调移动技术的关键作用。 2. **数据基础**： impactful AI依赖本地化、高质量数据，创新者通过移动端、物联网、遥感等多源数据构建数据管道，解决数据稀缺与碎片化问题（如KissanAI收集160万农业指令）。 3. **技术架构**：采用混合AI（预测+生成式）、开源模型（如Meta Llama）及边缘计算，适配低资源环境；模块化设计确保灵活性（如Digital Green的RAG管道）。 4. **商业模式**：以B2B/B2G为主（如Khushi Baby与政府合作），依赖“替代支付方”（企业/政府）克服用户支付能力限制，辅以SaaS订阅（如Trestle Labs）。 5. **关键案例**：KissanAI开发低成本农业SLM（Dhenu2），&frnds通过人机协同数字化供应链，验证了AI在LMICs的可行性与规模化路径。
数据从何而来？ AI如何落地？ 谁来买单？
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