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封面
市場調查報告書
商品編碼
2141146

人工智慧對企業連結和運算的影響

The Impact of AI on Enterprise Connectivity & Compute
出版日期: | 出版商: ABI Research | 英文 | 商品交期: 最快1-2個工作天內

價格
簡介目錄

具體益處：

  • 本報告闡述了專用 5G、Wi-Fi 6E/7、固網、工業乙太網、網路切片、5G 固定無線存取 (FWA) 和低地球軌道 (LEO) 衛星在企業 AI（人工智慧）連接架構中的作用。
  • 我們根據資訊科技 (IT) 領導層的優先事項（例如混合計算、本地工作負載保留、模組化現代化、管治和投資回報率 (ROI) 框架）來評估企業 AI 連接策略。
  • 本文比較了供應商在網路和安全、專用蜂窩網路、工業無線、邊緣基礎設施、互連、運算和人工智慧基礎設施等整個生態系統中的定位。

重要問題的答案：

  • 企業人工智慧將如何轉變基礎設施策略，從「雲端優先」部署轉向在設備、閘道器、本地、營運商邊緣和超大規模雲端環境中謹慎地分配工作負載？
  • 考慮到延遲、資料量、資料主權和成本限制，哪些 AI 工作負載應該部署在設備邊緣、閘道器邊緣、本地/私有雲端、運營商邊緣或超大規模雲端？
  • 企業在部署人工智慧和邊緣運算時，應該如何權衡專用蜂巢網路、Wi-Fi 6E/7、網路切片、5G FWA、固網、工業乙太網和低地球軌道衛星之間的利弊？
  • 隨著營運型人工智慧進入分散式企業環境，安全、網路、運算、營運技術 (OT)、Wi-Fi 和專用 5G 的邊界是如何融合的？

調查要點：

  • 來自高階企業決策者（包括資訊長(CIO)、技術長(CTO)、 IT基礎設施主管和 AI 策略領導者）的見解，涉及 AI 採用、邊緣 AI、實體 AI 工作負載部署、雲端和本地投資、安全性、合規性、管治、投資回報率和供應商選擇。
  • 評估跨裝置邊緣、閘道器邊緣、本地/私有雲端、營運商邊緣和超大規模雲端的工作負載部署。這包括延遲範圍、特定硬體範例、工作負載類型和資料傳輸模式。
  • 對涵蓋資料主權和合規性、彈性和自主性、延遲敏感度、頻寬成本和可用性以及「按需雲端」預設設定的四門工作負載放置框架進行評估。

本報告的主要主要讀者

  • 一家私人 5G、工業無線和企業網路供應商，致力於為製造、物流、能源、採礦、交通運輸和公共安全等營運環境開發人工智慧賦能的連接解決方案。
  • 運算、加速器、伺服器和 AI 基礎設施供應商正在調整其產品藍圖，以滿足企業對跨設備邊緣、閘道器邊緣、本地/私有雲端、電信邊緣和超大規模雲端的「工作負載友善部署」的需求。
  • 企業連接供應商的產品、策略和業務發展負責人，結合無線射頻 (RF)、網際網路協定 (IP) 網路、安全、營運技術 (OT) 整合和雲端的經濟性，建構由服務等級協定 (SLA) 支援的持續營運服務模式。

目錄

主要發現

主要預測

主要公司和生態系統

  • 對連接層的要求日益提高
  • 工作量分配策略
  • 本地部署邊緣運算的興起
  • 供應商策略
簡介目錄
Product Code: PT-4052

Actionable Benefits:

  • Identify where private 5G, Wi-Fi 6E/7, fixed line, industrial Ethernet, network slicing, 5G Fixed Wireless Access (FWA), and Low Earth Orbit (LEO) satellites fit within enterprise Artificial Intelligence (AI) connectivity architectures.
  • Benchmark enterprise AI connectivity strategies against Information Technology (IT) leadership priorities for hybrid compute, on-premises workload retention, modular modernization, governance, and Return on Investment (ROI) frameworks.
  • Compare vendor positioning across networking and security, private cellular, industrial wireless, edge infrastructure, interconnection, compute, and AI infrastructure ecosystems.

Critical Questions Answered:

  • How will enterprise AI shift infrastructure strategy from cloud-first deployment to deliberate workload placement across device, gateway, on-premises, telco edge, and hyperscale cloud environments?
  • Which AI workloads should remain at the device edge, gateway edge, on-premises/private cloud, telco edge, or hyperscale cloud based on latency, data volume, sovereignty, and cost constraints?
  • How should enterprises evaluate the trade-offs between private cellular, Wi-Fi 6E/7, network slicing, 5G FWA, fixed line, industrial Ethernet, and LEO satellite for AI and edge compute deployments?
  • How are security, networking, compute, Operational Technology (OT), Wi-Fi, and private 5G boundaries converging as operational AI moves into distributed enterprise environments?

Research Highlights:

  • Insights from senior enterprise decision makers, including Chief Information Officers (CIOs), Chief Technology Officers (CTOs), heads of IT infrastructure, and AI strategy leaders, on AI adoption, Edge AI, and Physical AI workload placement, cloud and on-premises investment, security, compliance, governance, ROI, and vendor selection.
  • Assessment of workload placement across device edge, gateway edge, on-premises/private cloud, telco edge, and hyperscale cloud, including latency ranges, hardware examples, workload types, and data movement patterns.
  • Evaluation of a four-gate workload placement framework covering data sovereignty and compliance, resiliency and autonomy, latency sensitivity, bandwidth cost and availability, and cloud-on-merit defaults.

Who Should Read This?

  • Private 5G, industrial wireless, and enterprise networking providers developing AI-ready connectivity offerings for operational environments such as manufacturing, logistics, energy, mining, transportation, and public safety.
  • Compute, accelerator, server, and AI infrastructure vendors aligning product roadmaps with enterprise demand for workload-right placement across device edge, gateway edge, on-premises/private cloud, telco edge, and hyperscale cloud.
  • Product, strategy, and business development leaders at enterprise connectivity providers building recurring, Service-Level Agreement (SLA)-backed operated service models that combine Radio Frequency (RF), Internet Protocol (IP) networking, security, OT integration, and cloud economics.

TABLE OF CONTENTS

Key Findings

Key Forecasts

Key Companies and Ecosystems

  • Rising Requirements on the Connectivity Layer
  • Workload Placement Strategy
  • Rise of the On-Prem Edge
  • Vendor Strategies