We are living on the edge, and the future of infrastructure is no exception. The combination of cloud and edge computing is paving the way for a new era of real-time intelligence. Speed, intelligence, and agility are no longer competitive advantages—they’re table stakes.
As data volumes grow — with the average large enterprise now managing 150 petabytes, expected to double by 2026 — and the pressure for real-time responsiveness increases, edge computing is emerging as a vital shift in how enterprises build and operate their infrastructure. According to Hitachi Vantara’s State of Data Infrastructure Global Report 2024, this surge in data is forcing organizations to rethink centralized architectures. No longer confined to the margins of IT strategy, edge computing is being recognized as the core enabler of innovation across industries as wide-ranging as manufacturing, healthcare, banking and retail.
Massive investments are spurring this edge-adoption momentum. IDC projects global spending on edge computing will grow at a compound annual growth rate of 13.8%, climbing to nearly $380 billion by 2028, up from $261 billion in 2025. The numbers are staggering, and there is no sign of this trend slowing down. As IT executives seek to trim margins while optimizing their data strategy, the demand for localized processing and analytics is expected to continue its growth spurt.
Why is proximity such a plus? The answer lies in the proverbial “distance from A to B.” Data infrastructure teams now achieve unmatched speed and lower latency for essential operations through data processing that occurs near data origins. The frictionless force multiplier provides organizations with real-time and actionable insights that drive operational optimization and innovation. The technology provides AI capabilities democratization through its ability to perform advanced processing near the network’s edge, rather than requiring extensive deployments.
Why edge, why now?
The limitations of conventional, centralized designs have been exposed by the explosion of real-time data. Sending every byte to a central cloud server is becoming increasingly impractical. Edge computing solves this problem by bringing analytics and computation closer to the source, enabling localized action without requiring round-trip latency.
Consider manufacturing and production. AI models deployed on-site help plants detect irregularities and forecast maintenance requirements, leading to more seamless operational continuity. Healthcare facilities leverage edge technology to deliver real-time patient monitoring via network bandwidth-independent methods. Financial institutions are turning to edge computing to power faster fraud detection, enable real-time transaction approvals, and deliver hyper-personalized customer experiences — all while meeting stringent regulatory and data residency requirements.
AI at the edge: from theoretical to practical
The implementation of edge computing technology transforms the way artificial intelligence operates. Most AI workloads previously required extensive cloud infrastructure to operate. AI functionality now operates at the edge due to improved computing and storage technology, creating new opportunities for applications that require low-bandwidth and low-latency operations.
From oil rigs and remote mines to rural healthcare clinics, organizations that embed AI into their edge operations are deriving insights locally and enabling autonomous decision-making where it matters most.
Security and compliance at the source
Speed and insight matter, but without strong security and governance at the edge, they’re not enough.
Workload distribution does not need to compromise security measures. Edge computing protects sensitive data through local processing that minimizes exposure during data transfers. Zero-trust architectures combined with data encryption and secure boot procedures have become standard practices that enhance the security of edge deployments by limiting access to authorized users, safeguarding data at rest and in transit, and ensuring only verified software can run on edge devices — reducing the attack surface across distributed environments.
As an added advantage, edge models ensure that entities comply with local regulations and data sovereignty laws, particularly in regions with stringent guidelines on the use of personal data.
Measurable impact across industries
The business case for edge is no longer theoretical. Delivery efficiency in logistics is increased when real-time routing decisions are made at the edge. In the energy sector, edge intelligence-powered smart grids enhance load balancing and shorten outages. Public safety organizations implement edge-based video analytics to enhance their incident detection and response capabilities for a faster response time.
These applications demonstrate that edge computing represents more than technological capabilities because it delivers measurable outcomes, which create competitive advantages.
What’s next: orchestration between edge and cloud
Looking ahead, the real opportunity lies in fluid orchestration between edge and cloud environments. Systems that facilitate the intelligent movement of workloads according to security, latency or regulatory requirements will provide the most significant ROI.
Forward-thinking organizations will embrace edge computing as a leap forward that complements, rather than replaces, centralized systems, enabling enterprises to act quickly, reduce risk and generate insights precisely where and when they’re needed.
Bottom line: IT leaders looking to future-proof their organizations would be wise to move their computing operations toward the edge.
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