全球通讯业者生成式人工智慧解决方案市场：成长机会（2025-2030）

Growth Opportunities for Telcos' Enterprise GenAI Solutions, 2025-2030

出版日期: 2025年08月08日 | 出版商:

Frost & Sullivan | 英文 33 Pages | 商品交期: 最快1-2个工作天内

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简介目录

利用大规模语言模式推动转型成长，实现人工智慧主导的企业产品与服务

生成式人工智慧 (GenAI) 是人工智慧的一个分支，指的是能够透过学习现有资料和模型来创建新内容（包括文字、图像、程式码、音讯和影片）的技术。对于通讯服务供应商而言，GenAI 提供的转型机会远远超过网路优化和客户支援。透过部署自有的大规模语言模型 (LLM) 并整合 AI主导的服务，通讯业者可以释放新的 B2B收益来源，并将自己定位为企业数位转型的策略合作伙伴。

虽然大多数通讯业者已启动人工智慧计划，但其成熟度各不相同，从概念验证验证到跨多个使用案例的大规模实施，不一而足。然而，一个重大障碍依然存在：缺乏统一的即时企业资料架构阻碍了模型训练，并限制了GenAI解决方案的有效性。清晰的人工智慧策略和蓝图，以及完善的数据准备，对于充分发挥人工智慧的潜力至关重要。

本报告探讨了通讯业者主导的企业 GenAI 解决方案的现状，对北美、欧洲和亚太地区最重要的通讯业者进行了基准测试，分析了新兴趋势和市场动态，强调了影响成长的关键推动因素和挑战，并确定了通讯业者开发行业特定的 AI 和数据管理服务的战略机会。

分析范围

AI是指模拟人类智慧并协助自我学习功能决策的技术，在通讯市场中指的是生成性AI。
GenAI 是企业 AI 解决方案市场中 AI 的一个子集。该平台透过学习通讯AI 市场中现有的数据和模型，产生新的内容，包括文字、图像、程式码、音讯和影片。
在北美通讯业者AI 应用市场，通讯服务供应商(通讯业者 ) 可以使用 GenAI 进行网路营运和客户服务，并部署自己的大规模语言模型 (LLM)，从而在 B2B 领域创造新的收益来源。我们能够与企业紧密合作，建立解决方案并整合基于 AI 的工具和服务，这使参与企业通讯市场生成式 AI 生态系统中的关键参与者。
大多数通讯业者已开始在企业 AI 解决方案市场中部署 AI 技术，但其成熟度各不相同，从概念验证到通讯AI 市场中多种 AI 用例的大规模部署。制定清晰的策略和蓝图对于 AI 的落地至关重要。很少有通讯业者拥有支援整合企业资料池（包括即时数据）的架构，这显示北美通讯业者AI 应用市场中支援 AI 应用的资料准备程度较低。这导致 GenAI 应用训练 AI 模型面临挑战，且 AI 结果效率低。
本报告概述了通讯业者目前在通讯生成式人工智慧市场中为其企业客户提供的 GenAI 产品、其发展趋势以及影响市场成长的驱动因素和因素。报告也为通讯业者在企业 AI 解决方案市场中探索特定产业的AI 和数据管理解决方案提供了机会。

三大策略重点对通讯业者企业 GenAI 解决方案的影响

创新经营模式

日益成熟的自然语言处理 (NLP) 和电脑视觉技术能够提供更可预测的结果，使通讯市场的生成式人工智慧能够融入新的 B2B 服务。传统的收益来源正在逐渐减少，通讯业者正在透过新的经营模式寻求成长。 GenAI 为 AI 即服务和虚拟解决方案等新模式提供了策略机会，从而在企业 AI 解决方案市场中创造经常性收益。这些模式使通讯业者能够更有效地将其数据、5G 和边缘资产收益。
由于通讯AI市场缺乏内部专业知识，企业将越来越多地寻求可扩展且安全的AI解决方案。通讯业者凭藉其覆盖范围和端到端能力，可以成为值得信赖的AI合作伙伴。新的经营模式对于在北美通讯业者AI应用市场中保持竞争力和相关性至关重要。这种转变将支持长期成长，同时加强电信业者在特定产业转型中的作用。

颠覆性技术

在通讯市场的生成式人工智慧领域，通讯业者正从消费第三方人工智慧转向利用自身资料开发通讯业者专用的LLM。这些专用的LLM支援特定领域的功能，例如自动化服务工作流程、网路最佳化和预测分析，从而推动企业人工智慧解决方案市场中高价值的B2B产品。透过建构这种模式，通讯业者正在减少对超大规模资料中心业者的依赖，加强数据管理，并将自己定位为通讯人工智慧市场中B2B客户数位转型的关键推动者。
北美通讯业者AI 应用市场中最先进的通讯业者正在开发针对其企业需求的 GenAI使用案例，涵盖从基于代理的 AI 到特定产业解决方案。未来五年，通讯业者将能够透过整合和商业化该框架，安全、负责任地扩展这些产品。此外，GenAI 功能将深度整合到电信业者基础设施中，使电信业者能够成为通讯市场中值得信赖的生成式 AI 数位转型合作伙伴。

竞争激烈程度

许多领先的通讯业者正在投资人工智慧卓越中心 (CoE)，采用先进的自助式分析技术，并积极升级云端基础的数据基础设施，以整合企业人工智慧解决方案市场中来自不同来源的数据。然而，在通讯人工智慧市场中，进展会因策略重点以及公司/市场的成熟度而有所不同。
未来五年，在通讯业者将从基础支援转向可扩展创新。这些投资将加速北美通讯业者人工智慧应用市场中人工智慧主导产品和服务的上市时间。人工智慧卓越中心将从实验中心发展成为协作式 B2B 解决方案开发的引擎。自助服务分析和云端数据平台将与 5G 和边缘运算相结合，使电讯市场的生成式人工智慧能够大规模提供智慧服务。

成长阻碍因素

人工智慧和机器学习演算法在企业人工智慧解决方案市场中的成功取决于企业可用数据的品质。干净且标准化的数据使人工智慧/机器学习技术能够在通讯市场中创造价值并带来积极的业务成果。对于北美通讯业者应用市场中大多数采用人工智慧的通讯业者而言，取得干净且可用的资料集是一项挑战。
GenAI 应用程式出现错误或误判的风险很高。不准确或虚假的资讯可能会损害商业决策。通讯市场的生成式 AI 需要仔细评估资料来源和工作流程、制定策略，并将 AI 与现有开发工具整合。
传统系统各自为政，很少有电信业者拥有支援统一企业资料池的架构，该资料池包含人工智慧 (AI) 可以使用的格式的即时资料。企业 AI 解决方案市场中基于 AI 的使用案例需要多种技术，并需要复杂的系统整合能力。因此，电信业者必须克服系统整合的挑战，才能在通讯AI 市场中有效运作 AI 工具。
出于隐私考量限制存取预先匿名的资料、智慧财产权问题、演算法缺乏透明度、演算法偏见以及工作安全疑虑等监管和道德问题可能会阻碍北美通讯业者AI采用市场中AI市场的成长。

驱动程式

随着核心服务收益成长的下滑，通讯业者的AI应用市场需要扩展并差异化其服务产品，才能在竞争激烈的市场中保持竞争力。 AI技术将协助通讯业者在企业AI解决方案市场提供数位化服务，进而抓住新的机会。
数位基础设施能够产生、处理和储存大量非结构化数据，这使得企业更容易采用人工智慧解决方案。云端处理的普及、无线通讯网路的快速扩张以及低成本感测器可靠性的不断提升，正在消除企业在采用人工智慧解决方案时面临的一些技术障碍。这使得IT和通讯人工智慧市场能够快速部署这些解决方案，同时降低人工智慧相关硬体和资讯技术(IT)基础设施的成本。
随着人工智慧和机器学习演算法以及法学硕士 (LLM) 的进步，北美通讯业者人工智慧采用市场的人工智慧解决方案正在提供更可预测的结果，实现自动化并提高效率。
预训练模型和低程式码开放原始码人工智慧工具的出现消除了技术障碍，并支援电讯市场生成人工智慧中各种规模的公司快速采用人工智慧解决方案。

比较主要的人工智慧倡议
Altice 为企业客户提供的 GenAI 服务
德国电信为企业客户提供 GenAI 服务
e&enterprise 为企业客户提供的 GenAI 服务
KT Corporation 为企业客户提供的 GenAI 服务
Lumen 为企业客户提供的 GenAI 服务
Orange Business 为企业客户提供 GenAI 服务
SK TELECOM 为企业客户提供 GenAI 服务
Telefonica 为企业客户提供 GenAI 服务
Verizon 为企业客户提供的 GenAI 服务
其他通讯业者则为企业客户提供的 GenAI 服务
其他日本通讯业者则为企业客户提供的 GenAI 服务
其他加拿大通讯业者为商业客户提供 GenAI 服务

成长机会

成长机会1：特定产业解决方案
成长机会2：专业服务
成长机会3：产品增强
成长机会4：加强广告

结论

后续步骤Next steps

简介目录

Product Code: KBC2-67

Large Language Models to Drive Transformational Growth and Enable AI-Driven Enterprise Products and Services

Artificial intelligence subset generative AI (GenAI) refers to technologies capable of creating new content, such as text, images, code, audio, and video, by learning from existing data and models. For telecommunications service providers (telcos), GenAI presents transformative opportunities beyond network optimization and customer support. By deploying proprietary large language models (LLMs) and integrating AI-driven services, telcos can unlock new B2B revenue streams and position themselves as strategic partners in enterprise digital transformation.

While most telcos have initiated AI, their maturity levels vary widely-from proof-of-concept stages to large-scale implementation across multiple use cases. However, a major barrier remains: the lack of unified, real-time enterprise data architectures hampers model training and limits the effectiveness of GenAI solutions. A well-defined AI strategy and roadmap, along with improved data readiness, are essential for realizing AI's full potential.

This report examines the current landscape of telco-led GenAI solutions for enterprise clients; benchmarks the most important telcos in North America, Europe, and Asia-Pacific; analyzes emerging trends and market dynamics; and highlights the key enablers and challenges shaping growth. It also identifies strategic opportunities for telcos to develop industry-specific AI and data management offerings.

Scope of Analysis

AI refers to technologies that emulate human intelligence and assist decision-making with self-learning capabilities in the generative AI in telecom market.
GenAI is a subset of AI in the enterprise AI solutions market. The platforms generate new content, such as text, images, code language, audio, or video, by learning from existing data and models in the AI in telecommunication market.
Telecommunications service providers (telcos) can use GenAI for network operations and customer service in the North America telco AI adoption market, as well as to deploy their own large language models (LLMs) and create new revenue streams in the B2B segment. Their ability to work closely with enterprises to build solutions and integrate AI-based tools and services make them important participants in the ecosystem of the generative AI in telecom market.
Most telcos have started implementing AI technology in the enterprise AI solutions market, but they are at different stages of maturity-from proofs of concept to deployment of multiple AI use cases at scale in the AI in telecommunication market. A clear strategy and roadmap articulation are crucial in the AI adoption journey. Few telcos have architectures that support integrated enterprise data pools, including data from real-time sources, indicating low data readiness to support AI applications in the North America telco AI adoption market. This results in difficulty training AI models for GenAI applications and ineffective AI outcomes.
This report provides a perspective on telcos' current GenAI offerings to enterprise customers in the generative AI in telecom market, trends in their evolution, and drivers and restraints impacting market growth. It also offers telcos opportunities to explore industry-specific AI and data management solutions in the enterprise AI solutions market.

The Impact of the Top 3 Strategic Imperatives on Telcos' Enterprise GenAI Solutions

Innovative Business Models

Why: Maturing natural language processing (NLP) and computer vision technologies deliver more predictable outcomes, enabling telcos in the generative AI in telecom market to embed them into new B2B offerings. Traditional revenue streams are eroding, pushing telcos to seek growth through new business models. GenAI provides a strategic opportunity for new models, such as AI-as-a-service and virtualized solutions, that unlock recurring revenue in the enterprise AI solutions market. These models allow telcos to more effectively monetize data, 5G, and edge assets.
Frost Perspective: Enterprises will increasingly demand scalable, secure AI solutions because of a lack of in-house expertise in the AI in telecommunication market. Telcos, with their reach and end-to-end capability, can become trusted AI partners. New business models are essential to stay competitive and relevant in the North America telco AI adoption market. This shift supports long-term growth while reinforcing telcos' role in industry-specific transformation.

Disruptive Technologies

Why: Telcos are shifting from consuming third-party AI to developing telecom-specific LLMs using proprietary data in the generative AI in telecom market. These specialized LLMs enable domain-specific capabilities, such as automated service workflows, network optimization, and predictive analytics, which can drive high-value B2B offerings in the enterprise AI solutions market. By building their models, telcos reduce dependency on hyperscalers, enhance data control, and position themselves as key enablers of B2B customers' digital transformation in the AI in telecommunication market.
Frost Perspective: The most advanced telcos are developing GenAI use cases tailored for enterprise needs, ranging from agentic AI to industry-specific solutions in the North America telco AI adoption market. Over the next 5 years, telcos will be better positioned to scale these offerings securely and responsibly as they consolidate and commercialize frameworks. Moreover, GenAI capabilities will integrate deeply with the telco infrastructure, enabling telcos to act as trusted digital transformation partners in the generative AI in telecom market.

Competitive Intensity

Why: Many leading telcos have invested in AI centers of excellence (CoEs), are adopting advanced self-service analytics, and are actively modernizing their cloud-based data infrastructure to integrate data from disparate sources in the enterprise AI solutions market. Progress varies, however, depending on strategic priorities and the maturity level of companies and markets in the AI in telecommunication market.
Frost Perspective: Over the next 5 years, telcos that have wisely invested in CoEs, self-service analytics, and data infrastructure will shift from foundational enablement to scaled innovation. These investments will result in faster time to market for AI-driven products and services in the North America telco AI adoption market. AI CoEs will evolve from experimental hubs to engines of B2B solution co-development. Self-service analytics and cloud-data platforms will integrate with 5G and edge, enabling intelligent services at scale in the generative AI in telecom market.

Growth Restraints

The success of AI and ML algorithms in the enterprise AI solutions market depends on the quality of the data available in the enterprise. Clean and standardized data enable AI/ML technologies to deliver value and positive business outcomes in the AI in telecommunication market. Accessing clean and usable datasets is challenging for most telcos that are adopting AI in the North America telco AI adoption market.
There is a high risk that GenAI applications will respond with errors and hallucinations. Inaccurate or fabricated information can compromise companies' decision-making. It is necessary to carefully evaluate data sources and workflows, formulate strategies, and integrate existing development tools with AI in the generative AI in telecom market.
Legacy systems operate in silos, and few telcos have an architecture that supports an integrated enterprise data pool, including real-time data in formats that AI can use. AI-based use cases in the enterprise AI solutions market will leverage multiple technologies, requiring complex system integration capabilities. Therefore, telcos must overcome system integration issues to run AI tools efficiently in the AI in telecommunication market.
Regulatory and ethical issues, such as privacy considerations that restrict access to data before anonymization, intellectual property issues, a lack of algorithm transparency, algorithm biases, and job security concerns, will hinder the AI market's growth in the North America telco AI adoption market.

Growth Drivers

With declining revenue growth from core services, telcos in the generative AI in telecom market must increase their offerings and create differentiation to remain relevant in a competitive market. AI technologies enable telcos to support new business opportunities by offering digital services in the enterprise AI solutions market.
Digital infrastructure's ability to generate, process, and store large volumes of unstructured data makes it easier for enterprises to implement AI solutions. The ubiquity of cloud computing, the rapid expansion of wireless communication networks, and the increasing reliability of low-cost sensors have removed some technical barriers that enterprises face in deploying AI solutions. This allows them to quickly implement these solutions with lower AI-related hardware and information technology (IT) infrastructure costs in the AI in telecommunication market.
With advancements in AI and ML algorithms and LLMs, AI solutions in the North America telco AI adoption market will offer more predictable outcomes, resulting in automation and higher efficiency.
The availability of pre-trained models and low-code and open-source AI tools will remove some technical barriers and support faster adoption of AI solutions across businesses of all sizes in the generative AI in telecom market.