2024年边缘计算状况报告_泛在AI赋能行业解锁现代化商业用例（英文）_27页_7mb

报告摘要

2024 State of Edge Computing Summary

Core Content

Edge computing is gaining significant traction across industries, driven by the need for low latency, security, and data volume management. The integration of AI and Generative AI (GenAI) is reshaping how businesses approach edge computing, leading to the concept of AI Anywhere, where AI capabilities are extended to the edge to meet mission-critical use cases. This convergence of edge, AI, and cloud technologies is expected to transform business processes, enhance security, and enable real-time analytics.

Main Points

1. Adoption Trends

All organizations plan to use edge computing in the next 12 months, with 100% of respondents indicating this intent.
Latency sensitivity and security are the top two use cases for edge computing.
Healthcare and life sciences are the most likely to avoid the cloud, with 25% not using it at all.
Edge deployments are increasing significantly, with 38% of banking institutions planning to deploy AI to hundreds of edge locations by 2026, while 71% of utilities will limit deployments to fewer than a hundred.

2. Investment and Spending

Enterprise organizations (revenues > $1B) are investing heavily in edge computing, with an average of $740K expected over the next 24 months.
Banking and finance will spend the most on edge, with an average of $670K, while utilities will spend the least, at $342K.
Cloud partners are the primary source of funding for edge computing, especially in retail and banking, with 61% and 51% of respondents, respectively, indicating reliance on cloud partners.
Large enterprises (revenues > $5B) are expected to spend $0.5–$5M on edge computing, with 56% spending in that range.

3. AI Anywhere Strategy

AI is becoming a key driver for edge computing, especially in mission-critical applications that require real-time processing, privacy, and latency.
GenAI is expected to unlock new skillsets and transformative capabilities, such as predictive analytics and visual inspection.
Customer service and technology and process development are the top departments deploying AI, with retail and banking leading in AI adoption.

4. Partnership Dynamics

Cloud partners are the most preferred for edge computing, but mid-market and SMBs tend to rely more on OEMs, system integrators, and MSPs.
Partner expertise is a concern for many, especially in North America, where the lack of edge services is the least ranked issue.
AI deployment is more partner-led than edge computing, with 42% of mid-market organizations relying on partners for AI.

5. Barriers to Edge Adoption

Cybersecurity and data protection risks are the top barriers to edge adoption, with 47% in North America and 41% in EMEA citing these concerns.
Skill shortages are a major issue for enterprises, while limited partner availability is a concern for mid-market and SMBs.
Retail and utilities are particularly affected by partner availability, with 17% of retail and 46% of utilities citing this as a barrier.

6. Industry-Specific Use Cases

Retail prioritizes fraud loss prevention and personalization, with 84% of edge workloads being business analytics.
Manufacturing focuses on predictive maintenance and inventory management, with 61% of edge workloads being asset tracking.
Healthcare emphasizes remote patient monitoring and data collection, with 91% expecting standardized software from the cloud to be essential for AI and edge deployment.
Public sector is focused on physical security and asset protection, with 71% expecting cost savings from edge computing.

Key Information

Edge computing is becoming essential for latency-sensitive and data privacy-driven applications.
GenAI is expected to drive new business models and capabilities, especially in customer service, security, and predictive analytics.
Cloud and open ecosystems are enabling developer agility, allowing for faster application development and deployment at the edge.
Enterprise organizations are leading in AI and edge investment, with 40% planning to invest more than $500M in edge computing.
Partnerships are crucial for scaling AI and edge deployments, though enterprise organizations are more likely to use cloud partners, while mid-market and SMBs rely on local and regional partners.

Conclusion

The 2024 State of Edge Computing highlights a growing reliance on edge computing across industries, driven by AI integration, latency requirements, and security needs. As organizations scale their edge and AI strategies, partner collaboration and standardized solutions will be essential to meet the increasing demand for on-premises compute, real-time processing, and data sovereignty. The convergence of edge, AI, and cloud is set to redefine business operations, customer experiences, and IT decision-making in the coming years.