2024 边缘计算现状：各行业如何利用随时随地的人工智能来解锁现代商业案例

COMMISSIONED BY Summary CATALYST OMDIA VIEW The increasing prominence of AI and Generative AI(GenAI) is anticipated to profoundly transform businessand the nature of work. This expectation resonatesacross all leadership levels, from the C-suite overseeingdigital transformation initiatives to practitioners activelytesting, executing, and scaling cutting-edge innovationsto deliver value. Edge computing is not new but has taken on a newlevel of importance with digitization of organizationsand consumers, increasing demands to processdata locally on-premises, and leveraging the latest AIand cloud-based innovation. IT decision makers andbuyers are exploring new use cases, and their vendorsare developing software and hardware to meet newrequirements for edge located equipment. GenAI will spawn a whole new approach to thinkingabout the nature of work and who and where itshould be performed. GenAI is poised to unlock newskillsets and present novel challenges, transformingvarious aspects of the business landscape throughelements such as creative problem solving, datainterpretation and analysis, and technical proficiencyin training and fine-tuning GenAI models. This willlead to the development of new devices and softwaretechnologies to improve business processes, relievehumans of repeatable tasks, and make life more fun. Several industry verticals have been leveraging edgecomputing for years to transform their operations, withmanufacturing using it for automation, while others,such as municipal governments, are just starting smartcity project rollouts. It is evident that we are only atthe beginning of exploring various use cases. As wecomplete the deployment of known applications, manyadditional use cases will undoubtedly emerge. At the same time, the speed and effectiveness of delivering value to customers andstaff locally requires compute processing on-premises where the collection and real-time processing of data are becoming increasingly important. As a result, latency andbandwidth are becoming key performance determinants and are driving the needfor better telecommunication networks and more computing power to be placedcloser to end users and machines. Security and data volume are also factors thatcan influence end users to place more compute at the edge. On-premises computing is well-established, but the integration of cutting-edgecloud infrastructure and AI represents a transformative shift. Cloud technologiesare empowering developers to create agile and responsive applications tailoredto evolving customer needs, both internal and external. Additionally, the swiftadvancements in AI and Generative AI are unlocking new opportunities to addresscomplex, costly, and resource-intensive challenges, such as predictive analytics andvisual inspection. The convergence of on-premises computing, cloud technology, andAI heralds a new era for IT decision-makers: AI Anywhere. Enterprises, telecommunications network providers, and cloud service providers(CSP) have adopted edge computing strategies. For example, CSP approaches arebased on the premise of deploying hardware to the edge that is connected to andcompatible with the core cloud solution offered by the cloud provider. KEY MESSAGES Adoption of edge is evolving driven by the need for low latency,security, and data volume requirements. Many enterprises have opted to own their own hardware at the edge instead ofrelying on CSP- or telco-operated data centers (DCs). In fact, enterprises were earlyadopters of edge computing. Many enterprises have a distributed business modelswhere application support is required at multiple branches, offices, or stores.Additionally, many enterprises have been running latency-sensitive workloads suchas healthcare and industrial applications; on-site data consolidation, data sharing,and analytics; and retail store management. With GenAI the development of theapplications requires in some cases, large amounts of compute resources suchas GPUs and TPUs. However, the execution of these applications is likely to bedistributed to the edge for privacy and latency reasons. AI anywhere is a key driver supporting mission critical use casesrequiring an inclusive edge, AI, and cloud strategy. Spend and scale is increasing in coverage across industries. Cloud and open ecosystems unlock developer agility to build,deploy, and scale applications at the edge. Industry View: Industries are unlocking new use cases toimprove business analytics, security, and operations. Adoption of edge is evolving 100% OF RESPONDENTS PLAN TO USE EDGECOMPUTING IN THE NEXT 12 MONTHS S7: WHICH WOULD YOU RATE AS THE TOP REASONS FOR EDGE COMPUTING DEPLOYMENT? Figure 1 shows that not all use cases are suitable for the public cloud. Considerthe following scenarios: zComputational Requirements:When processing must continueuninterrupted, even if the connection is down.zData Gravity and Volume:When handling high-volume, noisy, or costly datathat is time-consuming to tr