The global agentic AI for financial services market encompasses intelligent systems and platforms capable of autonomous action to achieve complex, predefined objectives within the financial industry.

The market by value in this report is defined as the revenues that enterprises gain from goods and/or services sold within the specified market and geography. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The global AI-powered radio access network market encompasses the integration of artificial intelligence and machine learning technologies within the cellular network infrastructure that connects end user devices to the core network. This technology functions by employing sophisticated algorithms to continuously analyze real time network data, enabling the autonomous optimization of RAN operations. Key applications include predictive analytics for network maintenance, intelligent traffic steering, dynamic energy consumption management, and automated resource allocation for 5G network slicing.

The global AI-ready cloud solutions market encompasses a specialized segment of the cloud computing industry, providing infrastructure, platforms, and services explicitly designed to support and scale artificial intelligence and machine learning workloads. These solutions are characterized by their high-performance computing capabilities, including access to specialized processors like GPUs and TPUs, and scalable storage and networking to handle massive datasets. AI-ready cloud solutions also feature integrated AI and machine learning frameworks, MLOps platforms for automating the machine learning lifecycle, and APIs for seamless integration of AI capabilities into existing applications. This market caters to the increasing demand from enterprises for agile, cost-effective, and powerful environments to develop, deploy, and manage AI-driven applications, thereby enabling innovations such as predictive analytics, natural language processing, and intelligent automation without the need for substantial upfront infrastructure investments.

观察海外 AI 产业的发展路径，我们发现从大厂密集投入 Capex（2023 年）到 Token 的爆发 （2025 年）并非一蹴而就，中间存在两年左右的滞后期。海外的时间线看，2023→2024→2025 分别经历了 Capex 高投入→模型密集迭代+云商收入回归高增→Token 高增的三个阶段。这背后的逻辑是因为 Capex 的高投入会先用于模型的训练中，大模型的训练十分昂贵对资金要求 较高。一直到 2025 年尤其是下半年开始，需求侧的核心指标 Token 数才开始高速增长。我们认为这背后的原因是随着持续两年的投入，许多场景如搜广推、Coding、多模态等逐步跑通。