AI 및 기계 학습 조사 서비스

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?