人工智慧和機器學習研究服務

AI & Machine Learning 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 的人工智慧（AI）和機器學習（ML）市場研究服務全面評估 AI 相關技術創造的商業機會，並將硬體和軟體技術堆疊連接起來，貫穿整個價值鏈，從嵌入式系統到資料中心。

人工智慧和機器學習目標領域

廣泛的AI/ML研究涵蓋資料、定性趨勢分析、預測、基準測試和分析報告。為供應商和終端用戶（涵蓋整個供應鏈）提供諮詢服務，內容包括塑造人工智慧和機器學習市場趨勢、經營模式和市場進入策略的關鍵技術和業務因素。從嵌入式運算到人工智慧資料中心，服務範圍涵蓋半導體創新、技術、平台即服務（PaaS）、軟體授權和終端應用。隨著人工智慧技術的日益普及，也為採用該技術的公司提供權威的市場進入（GTM）諮詢服務，並考慮各種可用於簡化工業和業務流程的AI/ML應用和用例。

AI/ML市場研究方法以應用案例為中心，並與技術採納緊密相關。除了家用電子電器和機器人等人工智慧已廣泛應用的行業外，還追蹤零售、製造、能源、汽車、公共安全和通訊等行業的AI/ML應用。尤其關注邊緣人工智慧解決方案。

機器學習（ML）
人工智慧（AI）
基於代理的人工智慧
生成式AI（Gen AI）
資料和預測分析
面向消費和工業應用的嵌入式人工智慧運算
資料中心基礎設施：運算、冷卻、網路、電源
邊緣人工智慧與雲端人工智慧的比較分析
演算法和硬體技術的分割
人工智慧工具和軟體開發工具包（SDK）分析
AI/ML領域的傑出技術創新者
邊緣AI/ML
AI/ML用例及應用市場細分與分類
AI/ML的各種實現方法
AI/ML經營模式和市場進入策略
AI/ML在通訊業的應用案例
AI/ML在製造業的應用案例
AI/ML在消費市場的應用案例
物聯網市場中的AI/ML應用案例
開放原始碼在新型應用和經營模式形成中的作用
語音和影像識別、機器視覺、自然語言處理、生成式和創造性對抗網路、自動推理以及安全應用領域的新趨勢

對人工智慧和機器學習的研究將推動技術創新和實用化。

對於創新者

在每個終端市場中找出新的應用場景。
查明具體情況。
這凸顯了嵌入式運算、本地部署和人工智慧資料中心領域的供應商的差異化機會。
監測監管和主權相關趨勢，並確定快速變化的人工智慧法規環境如何影響商業決策。
透過對 DeepSeek 的發布和主要貿易展上的公告等關鍵事件進行知情且平衡的分析，提供對市場趨勢的可操作見解。
將人工智慧趨勢與計算半導體、網路、分散式運算和開發者平台等相關領域的創新聯繫起來。

執行負責人

分析成功的AI夥伴關係、分銷模式和通訊，並將它們與對業務成果的理解緊密聯繫起來，以支持 GTM 策略的發展。
透過對各種經營模式、供應商概況和夥伴關係的詳細分析來支持負責人。
根據對產業挑戰、成功夥伴關係和關鍵用例的洞察，制定新興技術藍圖。
分析了嵌入式運算供應商、AI 伺服器 OEM（原始設備製造商）、AI 開發平台和其他相關利益者之間的競爭地位。

在人工智慧和機器學習方面的研究有助於解決關鍵相關人員。

資料中心基礎設施

有助於解決哪些類型的問題？
- 找出影響未來運算需求的潛在趨勢。
- 將整個人工智慧資料中心價值鏈的市場資訊整合起來。
- 評估成熟企業和新興企業的策略和市場進入模式。
主要有助於解答哪些類型的問題？
- 為了滿足人工智慧運算的需求，經營模式發生怎樣的變化？
- 冷卻和電力需求是什麼？

半導體供應商

有助於解決哪些類型的問題？
- 評估未來對人工智慧加速器（包括 GPU、ASIC 和 FPGA）的需求。
- 確定推動未來半導體需求的關鍵工作負載。
- 這有助於全面了解半導體市場的競爭格局。
- 了解新興人工智慧半導體供應商帶來的挑戰。
主要有助於解答哪些類型的問題？
- 哪些工作負載和產業推動對人工智慧運算的需求？
- 晶片供應商該如何定位自身，才能善加適應設備端人工智慧運算和雲端運算？
- 人工智慧推理和訓練如何影響處理器架構和效能要求？

簡介目錄

ABI Research’s Artificial Intelligence (AI) and Machine Learning (ML) market intelligence service horizontally assesses the opportunities created by AI-related technology, linking the hardware and software technology stacks across the entire value chain - from embedded to data center.

AI & Machine Learning Coverage Areas

Our extensive coverage of AI/ML includes data, qualitative trend analysis, forecasts, and benchmark and analysis reports. We consult tech vendors (across the supply chain) and end users on the key technical and business factors that are essential for shaping AI and ML market activity, business models, and GTM strategies. From embedded compute to AI data centers, this includes semiconductor innovations, technologies, and platforms as-a-Service, software licensing, and end applications. We also provide technology implementers with authoritative advisory services on their GTM, accounting for various AI/ML applications and use cases to leverage for streamlined industrial and business processes as AI technology becomes democratized.

Our approach to AI/ML market research is use case-centric and tightly coupled with technology implementation. Aside from verticals with existing AI implementations, such as consumer electronics and robotics, we also track AI/ML deployments in retail, manufacturing, energy, automotive, public safety, and telecommunications. Special attention is dedicated to edge AI solutions.

Machine Learning (ML)
Artificial Intelligence (AI)
Agentic AI
Generative Artificial Intelligence (Gen AI)
Data & predictive analytics
Embedded AI compute in consumer & industrial
Data center infrastructure: compute, cooling, networking, power
Analysis of edge AI versus cloud AI
Algorithms and hardware technologies segmentation
Analysis of AI tools and Software Development Kits (SDKs)
AI/ML hot technology innovators
Edge AI/ML
Market segmentation and taxonomy of AI/ML use cases and applications
Different implementation approaches of AI/ML
AI/ML business models and GTM strategies
AI/ML use cases in the telecoms industry
AI/ML use cases in the manufacturing industry
AI/ML use cases in the consumer market
AI/ML use cases in the IoT market
The role of open source in shaping new applications and business models
Emerging trends in speech/image recognition, machine vision, natural language processing, generative and creative adversarial networks, automated reasoning and security applications

AI & Machine Learning Research Powers Technology Innovation & Implementation

For Innovators

Identify emerging use
cases across end markets, such as AI vision models, Gen AI, and agentic systems.
Highlight differentiation opportunities for vendors addressing embedded compute, on-premise deployments, and AI data centers.
Monitor regulatory and sovereignty trends to inform how the rapidly changing AI regulatory space influences business decisions.
Provide actionable insights into market developments for informed and balanced analysis of significant events, such as the DeepSeek moment and major trade show announcements.
Connect AI trends to adjacent innovations in compute silicon, networking, distributed computing, and developer platforms.

For Implementers

Shape GTM strategy development by analyzing successful AI partnerships, distribution models, and messaging, tightly coupled with awareness of business outcomes.
Support strategists with detailed analyses of diverse business models, vendor profiles, and partnerships.
Inform emerging technology roadmaps with insights into industry pain points, successful partnerships, and important use cases.
Analyze competitive positioning across embedded compute vendors, AI server Original Equipment Manufacturers (OEMs), AI developer platforms, and other stake

Our AI & Machine Learning Research Helps Solve Real Business Challenges For Key Stakeholders

Data Center Infrastructure Vendors

What challenges can we help you solve?
- Identify underlying trends that affect future compute demand
- Unify market intelligence across the AI data center value chain
- Assess incumbent versus challenger strategies and GTM models
What key questions can we help you answer?
- How are business models changing to address the demand for AI compute?
- What are the cooling and power requirements?

Silicon Vendors

What challenges can we help you solve?
- Evaluate future demand for AI accelerators, including GPUs, ASICs, and FPGAs
- Inform key workloads shaping future silicon demand
- Gain broad visibility into the semiconductor competitive landscape
- Understand the challenge posed by startup AI silicon vendors
What key questions can we help you answer?
- Which workloads and industries are driving demand for AI compute?
- How should chip vendors position themselves for on-device AI compute versus cloud?
- How does AI inference and training shape processor architecture and performance requirements?