EDGE COMPUTING on modern server room background. Information technology and business concept for resource intensive distributed computing services.
Edge computing is poised to surpass centralized data centers as the primary data processing hub, fueled by AI demands and a surge in localized content needs.
Edge computing is rapidly emerging as the cornerstone of global data processing, with ReThink Technology Research projecting that 74% of the world’s data will be processed outside traditional data centers by the early 2030s. This shift is driven by the growing demand for AI-powered low-latency applications and the need for localized content, creating substantial opportunities for telecommunications operators and data center providers. By 2031, more than half of this data will be processed directly at the edge, transforming how data is managed and monetized.
AI and generative AI (GenAI) are accelerating the push toward edge computing, enabling faster decision-making and reducing network congestion. IDC forecasts that spending on edge computing will climb to $228 billion in 2024 and nearly $378 billion by 2028, driven by investments in hardware, software, and services for edge solutions.
Telecom operators are positioned to benefit significantly from this shift. The adoption of open radio access networks (RAN) and frameworks like the RAN Intelligent Controller (RIC) highlights their role in integrating edge computing into mobile networks. These advancements enable operators to optimize network performance while creating new revenue streams. ReThink predicts edge computing-related revenues will rise from $3.29 billion in 2023 to $29 billion by 2031, split evenly between operator and enterprise deployments.
See also: AI on the Edge: Bringing Intelligence Closer to the Source
Strategic Partnerships and Industry Impact
Telecom carriers and hyperscalers like Amazon Web Services, Google Cloud, and Microsoft Azure are leveraging their infrastructure to capitalize on edge opportunities. Carriers like T-Mobile and Verizon are integrating AI-driven edge solutions into their networks to support genAI workloads. At the same time, hyperscalers enhance edge computing with offerings like AWS Local Zones and Google’s Distributed Cloud.
Despite the broader tightening of IT budgets, edge computing investments remain resilient due to their strategic importance. Analysts anticipate that by decentralizing data processing, businesses will unlock new efficiencies and business models, solidifying edge computing’s role as a vital component of the digital economy.
Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do.
Best Practices for Deploying and Scaling Industrial AIArtificial Intelligence (AI) is transforming industrial operations, helping organizations optimize workflows, reduce downtime, and enhance productivity. Different industry verticals leverage AI in unique ways.Link to The Center for Adaptive Edge Intelligence
The Center for Adaptive Edge IntelligenceAdaptive edge intelligence brings real-time decision-making to the point of data creation, whether from sensors, machines, or cameras.Link to The Value of Vehicle Electrification
The Value of Vehicle ElectrificationElectric vehicles (EVs) present automakers with many design, engineering, and manufactu ring challenges.Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT
Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoTDigital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement.Link to Smart Manufacturing for Automotive
Smart Manufacturing for AutomotiveLeading a transportation revolution in autonomous, electric, shared mobility and connectivity with the next generation of design and development tools.Link to Center for Data Pipeline Automation
Center for Data Pipeline AutomationAs businesses become data-driven and rely more heavily on analytics to operate, getting high-quality, trusted data to the right data user at the right time is essential.Link to Center for Automated Integration
Center for Automated IntegrationThe goal of automated integration is to enable applications and systems that were built separately to easily share data and work together, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more.Link to Continuous Intelligence: Insights
Continuous Intelligence: InsightsDigital transformation requires continuous intelligence (CI). Today’s digital businesses are leveraging this new category of software which includes real-time analytics and insights from a single, cloud-native platform across multiple use cases to speed decision-making, and drive world-class customer experiences.
