As enterprise investment in artificial intelligence (AI) at the edge accelerates, a new wave of tools from Intel signals a strategic shift in how AI applications are deployed outside the data centre. Rather than building entirely new infrastructure, organisations are increasingly demanding ways to integrate AI into their existing systems, a requirement Intel is addressing with the launch of its Intel AI Edge Systems, Edge AI Suites, and Open Edge Platform.
These offerings mark a significant step in the evolution of edge computing, where the demands of latency, power efficiency and total cost of ownership present a radically different challenge from cloud-based deployments. According to Dan Rodriguez, Intel corporate vice president and general manager of the Edge Computing Group, “enterprises want to expand the use of AI in their existing infrastructure and workflows at the edge, ensuring they meet their total cost of ownership and achieve power and performance goals.”
Edge computing is no longer an abstract aspiration. By the end of 2025, Gartner estimates that half of enterprise-managed data will be processed at the edge, in locations such as hospitals, retail environments and industrial sites, rather than traditional cloud or data centers. At least half of those deployments, by 2026, are expected to involve machine learning.
For Intel, which claims more than 100,000 real-world edge implementations in collaboration with partners, the challenge is not simply to deliver raw compute power, but to tailor performance, power consumption and software integration to fit tightly constrained environments. These environments range from remote oil rigs and factory floors to high street retail branches, where AI capabilities must be deployed alongside legacy systems without disrupting operations.
Unlike cloud data centres, where dedicated AI infrastructure and abundant power are the norm, edge deployments face more stringent conditions. Space, energy, and budget limitations force system builders to innovate around a more fragmented and heterogeneous hardware landscape. Intel’s proposition is that open, standardised and modular platforms offer the most pragmatic way forward.
The new Intel AI Edge Systems provide original equipment manufacturers (OEMs) and original design manufacturers (ODMs) with standardised blueprints and verification tools optimised for use cases such as vision AI and generative AI. These are designed to be flexible across a range of power levels and performance requirements, helping customers avoid the complexity and costs of bespoke deployments.
Complementing this, the Edge AI Suites are industry-specific software development kits that give independent software vendors and integrators access to pre-validated applications and benchmarked sample code. With suites tailored to sectors such as retail, manufacturing, smart cities, and media and entertainment, Intel aims to reduce the development burden and accelerate time to deployment.
At the core of this strategy is the Open Edge Platform, a modular, open source system designed to manage edge AI at scale. With tools such as Intel’s vPro and Active Management Technology, partners can manage containerised workloads on edge devices remotely, reducing the need for on-site technical support. This capability is increasingly vital for enterprises seeking to deploy and maintain AI systems across distributed, global operations.
Beneath the surface, the battle for performance supremacy in edge AI is nuanced. Metrics such as tera operations per second (TOPs) have often been used as shorthand for AI capability. But Intel argues that real-world performance depends more on end-to-end system optimisation. In a recent benchmark, it claims its Core Ultra processors delivered up to 2.3 times better pipeline performance, and up to five times better performance per dollar, compared to unnamed rivals that may have led in raw TOPs.
That distinction matters. As AI moves out of the lab and into everyday operational contexts, enterprises are discovering that the constraints of the real world rarely match the assumptions of the cloud. An open, collaborative and standards-based approach may prove to be the only sustainable way to deploy intelligent systems at the edge, especially as organisations look to scale their AI strategies while keeping costs and complexity under control.
In this context, Intel’s decision to double down on an open edge ecosystem represents more than a product launch. It is an acknowledgement of where AI is heading: not as a centralised capability, but as a distributed utility that must work across the messy, legacy-rich environments of modern enterprise infrastructure.
