Actionable Benefits
- Identify where edge Artificial Intelligence (AI) workloads are likely to be deployed across devices, Radio Access Networks (RANs), regional edge, and core infrastructure, and assess the role operators can play at each tier.
- Understand how operators can monetize edge AI through compute hosting, connectivity, workload placement, orchestration, and enterprise services.
- Evaluate the enterprise applications and industry verticals most likely to drive demand for distributed AI infrastructure.
Research Highlights
- Forecasts of edge AI chipset market growth and analysis of the implications for telco infrastructure.
- Assessment of compute placement across network tiers, including the infrastructure requirements and operator opportunities at each level.
- Analysis of enterprise demand across manufacturing, logistics, and utilities, including emerging edge AI applications.
- Review of vendor strategies across the edge AI and 6G ecosystem, covering AMD, Ericsson, Huawei, Intel, Nokia, Qualcomm, NVIDIA, and ZTE.
- Recommendations for operators on infrastructure priorities, commercial partnerships, and the transition toward AI-enabled 6G networks.
Critical Questions Answered
- How will edge AI infrastructure and chipset markets develop between 2026 and 2031?
- Where will AI workloads be processed, and what determines the choice between device, RAN, regional edge, and core infrastructure?
- What advantages can telco operators bring to the edge AI ecosystem, and where are they best positioned to compete?
- Which monetization models offer operators the strongest opportunities to capture value from enterprise edge AI demand?
- How will 5G-Advanced and 6G support the evolution of distributed AI workloads?
Who Should Read This?
- Telco operators, particularly executives and strategy, network infrastructure, cloud, and enterprise business teams assessing edge AI investment and monetization opportunities.
- Network equipment vendors, chipset providers, and cloud infrastructure companies developing products and strategies for distributed AI workloads.
- Enterprise technology decision makers in manufacturing, logistics, and utilities evaluating edge AI deployment opportunities.
Companies Mentioned
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