電信業AI研究服務

Telco AI Research Service

出版日期: 年間契約型資訊服務 | 出版商:

ABI Research | 英文

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簡介目錄

ABI Research電信業AI研究服務，旨在為通訊業的AI應用流程以及AI平台提供指導和洞察。服務內容涵蓋通訊業AI應用在公共雲端、混合雲端和私有雲端的競爭趨勢。此外，該服務還涉及核心網路技術、虛擬化和雲端原生平台，以及為通訊業AI模型訓練和推理提供必要資料的資料相關架構。

電信業AI的研究領域

我們的電信人工智慧研究服務，包括數據、預測和供應商基準報告，提供業界領先的研究和指導，幫助行動網路營運商 (MNO) 轉型為人工智慧企業。與雲端原生網路和人工智慧 (AI)/機器學習 (ML) 相關的主題包括生成式人工智慧 (Gen AI)、虛擬化平台和雲端原生技術。

除了技術研究之外，ABI Research 也特別注重業務和營運轉型挑戰，其首要任務是幫助通訊業者應對主權人工智慧和企業人工智慧等新興領域。

電信業AI應用的市場趨勢與預測
面向通訊業者的平台資料集，適用於公有雲、私有雲和混合雲端部署
對新興人工智慧和雲端原生領域（包括通訊網路元素的公共雲端部署）進行競爭分析、供應商基準測試和市場佔有率分析。
通訊業者人工智慧應用區域趨勢
通訊業者的 AI商業化戰略，包括圖形處理單元即服務 (GPUaaS)。
人工智慧在通訊網路中的應用案例，包括網路自動化。
電信業AI模式生命週期：邊緣訓練、推理與部署
通訊業者核心人工智慧部署準備度指數
從核心網路到無線接取網路（RAN），閉合迴路自動化在通訊網路中的潛力。
將通訊業者的 AI 供應商分類，範圍從營運支援系統(OSS) 和業務支撐系統 (BSS) 到無線系統。
虛擬化和雲端原生網路部署及其影響
面向通訊業者的高階雲端功能：網路切片和服務鏈
網路切片和5G網路
通訊雲端中的開放原始碼和生態系統
巨量資料和機器學習在通訊分析的應用
服務發布平台

一項關於人工智慧在通訊業的調查正在推動技術創新和實施。

對於創新者

行動網路營運商與人工智慧基礎設施能力之間的合作：這為通訊業的人工智慧需求、未來發展路徑和部署策略提供了清晰的見解。
確定人工智慧貨幣化實施案例：這涉及確定全球通訊業者和主要通訊業者對 GPUaaS 的採用。
追蹤電信業AI應用案例的新興應用案例和需求徵兆：我們監測通訊業者如何利用人工智慧實現網路切片和應用程式介面 (API) 等新服務，以及他們如何利用雲端原生平台和公共雲端來實現這一目標。
競爭格局評估：我們提供對競爭對手的策略、差異化優勢和市場定位的詳細分析，以支援策略規劃。
了解哪些技術將成為主流，哪些不會：確定通訊業者在人工智慧領域應該投資哪些技術，以及應該長期投資哪些技術。
生態系統協作指南：確定推動企業連結採用的關鍵合作夥伴、平台和整合商，並為生態系統協作策略提供指南。
加快產品上市速度：我們協助您更快、更自信地開發產品，讓您了解公司目前面臨的挑戰、優先事項和部署計畫。深入了解客戶面臨的挑戰，最佳化您在通訊業的 AI 策略。

為實施業務

明確技術差異化因素：透過提供企業自主人工智慧需求的詳細分析和見解，通訊業者的部署負責人可以選擇最佳部署策略。
了解市場規模和未來機會：透過這項服務，通訊業者將能夠了解每個技術領域，特別是人工智慧驅動的貨幣化相關技術（包括網路切片和 API 平台）在未來將成長多少。
確定關鍵網路領域的主導供應商：確定通訊人工智慧領域的主導供應商，並確定那些可能幫助行動通訊業者在競爭格局中保持地位的供應商。
確定哪些技術將被淘汰以及何時淘汰：通訊業者網路是由多種傳統技術組成的。我們的服務可以幫助通訊業者經營團隊了解哪些技術最有可能被人工智慧取代或增強，從而幫助他們最佳化部署。
評估閉合迴路自動化何時實現：根據當前市場狀況，確定網路自動化何時實施以及其進展方式。這將負責人選擇供應商並確定人工智慧實施方向。

我們利用人工智慧技術為通訊業進行研究，幫助關鍵相關人員解決現實世界的業務挑戰。

雲端服務供應商

能幫助解決哪些類型的問題？
- 這將有助於明確如何應對通訊業中的人工智慧商業機會。
- 我們將使我們的雲端策略與通訊業的 AI 優先事項保持一致。
- 這將有助於我們了解通訊業者如何發展其雲端相關計劃，為 6G 做好準備。
- 本報告明確了通訊業者在自主人工智慧領域應扮演的角色，以及他們應該如何與其他實體合作或競爭。
它主要可以幫助解答哪些類型的問題？
- 通訊業者將如何在公有雲、私有雲和混合雲端環境中部署人工智慧功能？
- 通訊業者將在人工智慧/機器學習領域，尤其是在生成式人工智慧領域，扮演什麼角色？
- 在人工智慧驅動的通訊業者網路中，公共雲端和私有雲端之間存在著什麼樣的差異？
- 6G將如何改變通訊業者網路中人工智慧的發展趨勢？

IT供應商

能幫助解決哪些類型的問題？
- 這將有助於了解通訊業在人工智慧基礎設施領域究竟佔多麼大的市場。
- 對於邊緣人工智慧，我們將確定通訊業者將部署哪種架構（x86、Arm 或圖形處理單元 (GPU)）。
- 我們將使通訊業者的投資策略與基礎設施的功能保持一致。
它主要可以幫助解答哪些類型的問題？
- 通訊業者會繼續部署私有雲端用於人工智慧，還是會遷移到公共雲端？
- 未來，哪些解決方案對通訊業者管理資料中心工作負載會比較有用？
- 通訊業者會繼續自行管理網路，還是會將大部分技術能力外包？

通訊業者的基礎設施供應商

能幫助解決哪些類型的問題？
- 我們將根據企業和通訊業者的AI需求調整我們的產品藍圖。
- 我們將聚焦在快速發展的人工智慧技術領域。
- 我們從獨立（SA）和 API 的角度區分了我們為通訊業者提供的 AI 基礎設施能力。
- 本研究將探討超大規模資料中心業者和新型雲端服務供應商如何在人工智慧領域與通訊業者競爭和合作。
它主要可以幫助解答哪些類型的問題？
- 通訊業者是如何實施閉合迴路自動化的？
- 通訊業者人工智慧市場規模有多大？供應商應該重點關注哪些領域？
- 一級供應商將如何制定差異化的策略？
- 超大規模資料中心業者將對這一技術領域產生什麼影響？
- 6G將為通訊業的AI帶來哪些創新？

通訊業者

能幫助解決哪些類型的問題？
- 我們將改善面向通訊業者的人工智慧實施策略。
- 我們最佳化與網路和人工智慧供應商的夥伴關係。
- 找出通訊業的AI創新。
- 我們將評估實作 GPUaaS 的可行性及其經營模式。
- 我們將確定閉合迴路自動化的可行性及其實施時機。
它主要可以幫助解答哪些類型的問題？
- 通訊業者採用GPUaaS的速度會有多快？
- 隨著我們邁向 6G，人工智慧在通訊業的預期發展路徑是什麼？
- 新的人工智慧概念將如何影響通訊業者的收入？
- 通訊業者的API會成功嗎？
- 人工智慧將如何以及在何處實現網路切片？
- 通訊業者在人工智慧應用的核心方面應該將精力集中在哪些方面？

簡介目錄

ABI Research’s Telco AI Research Service provides guidance and insight into the telco Artificial Intelligence (AI) journey and the platforms used to enable AI, including competitive dynamics of public, hybrid, or private clouds for telco AI payloads. The service also covers core network technologies, virtualized and cloud-native platforms, and data related architectures that provide the necessary data for telco AI model training and inference.

Telco AI Coverage Areas

Our Telco AI Research Service coverage, which includes data, forecasts, and vendor benchmarking reports, provides industry-leading research and guidance on the transformation of the Mobile Network Operator (MNO) into a AI company. Cloud-native networks and Artificial Intelligence (AI)/Machine Learning (ML) topics include Generative Artificial Intelligence (Gen AI), virtualized platforms, and cloud-native technologies.

ABI Research exercises a sharp focus on business and operational transformation issues, in addition to technical coverage with a priority on helping telco operators address the emerging sovereign and enterprise AI domains.

Market trackers and forecasts for telco AI deployments
Telco platform datasets for public, private, and hybrid cloud deployments
Competitive analysis, vendor benchmarks, and market shares for emerging AI and cloud-native areas, including public cloud deployments for telco network elements
Regional trends for telco AI deployments
Telco AI monetization strategies, including Graphics Processing Unit-as-a-Service (GPUaaS)
AI use cases in telco networks, including network automation
AI model lifecycle in telco: training, inference, and deployment at the edge
Core AI readiness index for telcos
Closed-loop automation potential in telco networks, from core to Radio Access Network (RAN)
Telco AI vendor taxonomy from Operations Support System (OSS)/Business Support System (BSS) to radio
Virtualized and cloud-native network deployment and implications
Advanced telco cloud features: network slicing and service chaining
Network slicing and 5G networks
Open source and ecosystems in telco cloud
Big data and ML for telco analytics
Service exposure platforms

Telco AI Research Powers Technology Innovation & Implementation

For Innovators

Bridge MNO with AI Infrastructure Capabilities: Provide clear insight into telco AI requirements and future evolution paths and deployment strategies.
Identify Successful AI Deployments for Monetization: Identify how GPUaaS is being deployed by telcos and who are the leading operators around the world.
Track Emerging Use Cases and Demand Signals for Telco AI Use Cases: Monitor how telcos aim to use AI to enable new services like network slicing and Application Programming Interfaces (APIs), and how they will utilize cloud-native platforms, and the public cloud to enable these.
Evaluate Competitive Landscape: Offer detailed analysis of competitor strategies, differentiators, and market positioning to support strategic planning.
Understand Which Technologies Will Become Mainstream and Which Ones Will Not: Identify which technologies to invest in for the telco AI domain and which to invest in for the long term.
Inform Ecosystem Collaboration: Highlight key partners, platforms, and integrators driving enterprise connectivity adoption to guide ecosystem engagement strategies.
Accelerate Time to Market: Support faster and more confident product development through access to current enterprise pain points, priorities, and deployment timelines. Optimize your telco AI strategy by understanding your customer pain points in detail.

For Implementers

Clarify Technology Differentiators: By providing detailed analysis and insights into enterprise requirements for sovereign AI, telco implementers will be able to select the optimal deployment strategy.
Understand Market Size and Future Opportunities: Through this service, telcos will be able to understand how big each technology domain will become, especially monetization-related technologies that are driven by AI and include network slicing and API platforms.
Understand Which Vendors Lead in Critical Network Areas: Identify which vendors lead in the telco AI domain and which ones are likely to help mobile operators remain relevant in the highly competitive telco environment.
Identify Which Technologies Will Sunset and When: The telco network is a patchwork of legacy technologies. Our service allows telco executives to understand which of these will likely be replaced or augmented by AI, helping them optimize their deployments.
Assess When Closed-Loop Automation Will Take Place: Identify when network automation will be deployed and how it will progress from the current state of the market. This will allow implementers to choose vendors and which direction to follow for AI adoption.

Our Telco AI Research Helps Solve Real Business Challenges For Key Stakeholders

Cloud Providers

What challenges can we help you solve?
- Identify how to address opportunities in the telco vertical for AI.
- Align cloud strategy with telco AI priorities.
- Understand how telcos will evolve their cloud-related efforts toward 6G.
- Identify what role telcos will play in the sovereign AI domain and how to partner or compete with them.
What key questions can we help you answer?
- How will telcos deploy AI capabilities in the public, private, and hybrid cloud domains?
- What role will telcos play in the AI/ML domain, especially in Gen AI?
- What is the dichotomy between public and private clouds in telco networks for AI?
- How will 6G change AI dynamics in telco networks?

IT Vendors

What challenges can we help you solve?
- Understand how big the telco vertical will be for AI infrastructure.
- Identify what platforms telcos will deploy, between x86, Arm, and Graphics Processing Units (GPUs) for AI at the edge.
- Align telco investment strategies with infrastructure capabilities.
What key questions can we help you answer?
- Will telcos continue to deploy private clouds or shift to the public cloud for AI?
- What solutions will help telcos manage their data center workloads in the future?
- Will telcos continue to manage their networks or will they outsource a large part of their technology capabilities?

Telco Infrastructure Vendors

What challenges can we help you solve?
- Align product roadmaps with enterprise and telco AI needs.
- Identify high-growth AI technology areas.
- Differentiate infrastructure capabilities for telco AI in the context of Standalone (SA) and APIs.
- Identify how hyperscalers and neoclouds will compete or cooperate with telcos for AI.
What key questions can we help you answer?
- How are telcos implementing closed-loop automation?
- How big is the market for telco AI and which areas should vendors focus on?
- How will strategies of Tier One vendors differentiate?
- How will hyperscalers affect this technology domain?
- What innovation will 6G bring to telco AI?

Telcos

What challenges can we help you solve?
- Improve telco AI deployment strategy.
- Optimize network and AI vendor partnerships.
- Identify telco AI innovation.
- Assess viability of GPUaaS deployment and business models.
- Identify if and when closed loop automation is viable.
What key questions can we help you answer?
- How fast will telcos adopt GPUaaS?
- What is the evolution path of telco AI toward 6G?
- What new AI concepts will make an impact on telco revenue?
- Will telco APIs succeed?
- How will AI enable network slicing and where?
- Where should telcos focus regarding the core for AI applications?