全球通訊業者生成式人工智慧解決方案市場：成長機會（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個工作天內

價格

簡介目錄

利用大規模語言模式推動轉型成長，實現人工智慧主導的企業產品與服務

生成式人工智慧（GenAI）是人工智慧的一個分支，指的是能夠透過學習現有資料和模型來創建新內容（包括文字、圖像、程式碼、音訊和影片）的技術。對於通訊服務供應商而言，GenAI 提供的轉型機會遠遠超過網路最佳化和客戶支援。透過部署自有的大規模語言模型（LLM）並整合 AI主導的服務，通訊業者可以釋放新的B2B收益來源，並將自己定位為企業數位轉型的策略合作夥伴。

雖然大多數通訊業者已啟動人工智慧計劃，但其成熟度各不相同，從概念驗證驗證到跨多個使用案例的大規模實施，不一而足。然而，一個重大障礙依然存在：缺乏統一的即時企業資料架構阻礙了模型訓練，並限制了GenAI解決方案的有效性。清晰的人工智慧策略和藍圖，以及完善的資料準備，對於充分發揮人工智慧的潛力非常重要。

本報告探討了通訊業者主導的企業 GenAI 解決方案的現狀，對北美、歐洲和亞太地區最重要的通訊業者進行了基準測試，分析了新興趨勢和市場動態，強調了影響成長的關鍵推動因素和挑戰，並確定了通訊業者開發行業特定的AI 和資料管理服務的戰略機會。

分析範圍

AI是指模擬人類智慧並協助自我學習功能決策的技術，在通訊市場中指的是生成性AI。
GenAI 是企業 AI 解決方案市場中 AI 的一個子集。該平台透過學習通訊AI 市場中現有的資料和模型，產生新的內容，包括文字、圖像、程式碼、音訊和影片。
在北美通訊業者AI 應用市場，通訊服務供應商（通訊業者）可以使用 GenAI 進行網路營運和客戶服務，並部署自己的大規模語言模型（LLM），在 B2B 領域創造新的收益來源。能夠與企業緊密合作，建立解決方案並整合基於 AI 的工具和服務，這使參與企業通訊市場生成式 AI 生態系統中的關鍵參與者。
大多數通訊業者已開始在企業 AI 解決方案市場中部署 AI 技術，但其成熟度各不相同，從概念驗證到通訊AI 市場中多種 AI 用例的大規模部署。制定清晰的策略和藍圖對於 AI 的落地非常重要。很少有通訊業者擁有支援整合企業資料池（包括即時資料）的架構，這顯示北美通訊業者AI 應用市場中支援 AI 應用的資料準備程度較低。這導致 GenAI 應用訓練 AI 模型面臨挑戰，且 AI 結果效率低。
本報告概述了通訊業者目前在通訊生成式人工智慧市場中為其企業客戶提供的GenAI 產品、其發展趨勢以及影響市場成長的促進因素和因素。報告也為通訊業者在企業 AI 解決方案市場中探索特定產業的AI 和資料管理解決方案提供了機會。

三大策略重點對通訊業者企業 GenAI 解決方案的影響

創新經營模式

日益成熟的自然語言處理（NLP）和電腦視覺技術能夠提供更可預測的結果，使通訊市場的生成式人工智慧能夠融入新的B2B 服務。傳統的收益來源逐漸減少，通訊業者透過新的經營模式尋求成長。 GenAI 為 AI 即服務和虛擬解決方案等新模式提供了策略機會，在企業 AI 解決方案市場中創造經常性收益。這些模式使通訊業者能夠更有效地將其資料、5G 和邊緣資產收益。
由於通訊AI市場缺乏內部專業知識，企業將擴大尋求可擴展且安全的AI解決方案。通訊業者憑藉其覆蓋範圍和端到端能力，可以成為值得信賴的AI合作夥伴。新的經營模式對於在北美通訊業者AI應用市場中保持競爭力和相關性非常重要。這種轉變將支持長期成長，同時加強電信業者在特定產業轉型中的作用。

顛覆性技術

在通訊市場的生成式人工智慧領域，通訊業者正從消費第三方人工智慧轉向利用自身資料開發通訊業者專用的LLM。這些專用的LLM支援特定領域的功能，例如自動化服務工作流程、網路最佳化和預測分析，推動企業人工智慧解決方案市場中高價值的B2B產品。透過建構這種模式，通訊業者減少對超大規模資料中心業者的依賴，加強資料管理，並將自己定位為通訊人工智慧市場中B2B客戶數位轉型的關鍵推動者。
北美通訊業者AI 應用市場中最先進的通訊業者開發針對其企業需求的GenAI使用案例，涵蓋從基於代理的AI 到特定產業解決方案。未來五年，通訊業者將能夠透過整合和商業化該框架，安全、負責任地擴展這些產品。此外，GenAI 功能將深度整合到電信業者基礎設施中，使電信業者能夠成為通訊市場中值得信賴的生成式 AI 數位轉型合作夥伴。

競爭激烈程度

許多領先的通訊業者投資人工智慧卓越中心（CoE），採用先進的自助式分析技術，並積極升級雲端基礎的資料基礎設施，以整合企業人工智慧解決方案市場中來自不同來源的資料。然而，在通訊人工智慧市場中，進展會因策略重點以及公司/市場的成熟度而有所不同。
未來五年，在通訊業者將從基礎支援轉向可擴展創新。這些投資將加速北美通訊業者人工智慧應用市場中人工智慧主導產品和服務的上市時間。人工智慧卓越中心將從實驗中心發展成為協作式 B2B 解決方案開發的引擎。自助服務分析和雲端資料平台將與 5G 和邊緣運算相結合，使電訊市場的生成式人工智慧能夠大規模提供智慧服務。

成長阻礙因素

人工智慧和機器學習演算法在企業人工智慧解決方案市場中的成功取決於企業可用資料的品質。乾淨且標準化的資料使人工智慧/機器學習技術能夠在通訊市場中創造價值並帶來積極的業務成果。對於北美通訊業者應用市場中大多數採用人工智慧的通訊業者而言，取得乾淨且可用的資料集是一項挑戰。
GenAI 應用程式出現錯誤或誤判的風險很高。不準確或虛假的資訊可能會損害商業決策。通訊市場的生成式 AI 需要仔細評估資料來源和工作流程、制定策略，並將 AI 與現有開發工具整合。
傳統系統各自為政，很少有電信業者擁有支援統一企業資料池的架構，該資料池包含人工智慧（AI）可以使用的格式的即時資料。企業 AI 解決方案市場中基於 AI 的使用案例需要多種技術，並需要複雜的系統整合能力。因此，電信業者必須克服系統整合的挑戰，才能在通訊AI 市場中有效運作 AI 工具。
出於隱私考量限制存取預先匿名的資料、智慧財產權問題、演算法缺乏透明度、演算法偏見以及工作安全疑慮等監管和道德問題可能會阻礙北美通訊業者AI採用市場中AI市場的成長。

促進因素

隨著核心服務收益成長的下滑，通訊業者的AI應用市場需要擴展並差異化其服務產品，才能在競爭激烈的市場中保持競爭力。 AI技術將協助通訊業者在企業AI解決方案市場提供數位化服務，進而抓住新的機會。
數位基礎設施能夠產生、處理和儲存大量非結構化資料，這使得企業更容易採用人工智慧解決方案。雲端處理的普及、無線通訊網路的快速擴張以及低成本感測器可靠性的不斷提升，消除企業在採用人工智慧解決方案時面臨的一些技術障礙。這使得IT和通訊人工智慧市場能夠快速部署這些解決方案，同時降低人工智慧相關硬體和資訊技術（IT）基礎設施的成本。
隨著人工智慧和機器學習演算法以及法學碩士（LLM）的進步，北美通訊業者人工智慧採用市場的人工智慧解決方案提供更可預測的結果，實現自動化並提高效率。
預訓練模型和低程式碼開放原始碼人工智慧工具的出現消除了技術障礙，並支援電訊市場生成人工智慧中各種規模的公司快速採用人工智慧解決方案。

比較主要的人工智慧舉措
Altice 為企業客戶提供的GenAI 服務
Deutsche Telekom為企業客戶提供 GenAI 服務
e&enterprise 為企業客戶提供的GenAI 服務
KT Corporation 為企業客戶提供的GenAI 服務
Lumen 為企業客戶提供的GenAI 服務
Orange Business 為企業客戶提供 GenAI 服務
SK TELECOM 為企業客戶提供 GenAI 服務
Telefonica 為企業客戶提供 GenAI 服務
Verizon 為企業客戶提供的GenAI 服務
其他通訊業者則為企業客戶提供的GenAI 服務
其他日本通訊業者則為企業客戶提供的GenAI 服務
其他加拿大通訊業者為商業客戶提供 GenAI 服務

成長機會

成長機會1：特定產業解決方案
成長機會2：專業服務
成長機會3：產品增強
成長機會4：加強廣告

結論

後續步驟

簡介目錄

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.