Presented by Arm
Artificial intelligence is no longer restricted to the cloud and data centers. Increasingly, it operates directly at the point of data creation – in devices, sensors and edge networks. This shift toward intelligence embedded in devices is driven by concerns about latency, privacy, and cost that companies face as they continue to invest in artificial intelligence.
For leadership teams, the opportunity is clear, says Chris Bergey, senior vice president and general manager of Arm’s customer division: invest in AI-powered platforms that complement cloud usage, provide real-time responsiveness, and protect sensitive data.
“With the explosion of connected devices and the rise of the Internet of Things, edge AI provides organizations with a significant opportunity to gain competitive advantage through faster and more efficient AI,” explains Bergey. “First movers are not only improving efficiency, they are redefining customer expectations. AI is becoming a differentiator in terms of trust, responsiveness and innovation. The sooner a company makes AI central to its workflows, the faster it will expand this advantage.”
Apply Cases: Deploying AI where the data lives
Enterprises are discovering that edge AI isn’t just about productivity gains – it’s a modern operating model. On-premise computing means less reliance on the cloud and faster, more secure, real-time decision-making.
For example, a factory floor can instantly analyze equipment data to prevent downtime, while a hospital can safely run diagnostic models on-site. Retailers are implementing in-store analytics using computer vision, while logistics companies are using on-device AI to optimize fleet operations.
Instead of sending massive amounts of data to the cloud, organizations can analyze and act on insights where they appear. The result is a more responsive, privacy-preserving, and cost-effective AI architecture.
Consumer expectation: Immediacy and trust
Working with the Alibaba Taobao team, China’s largest e-commerce platform, Arm (Nasdaq:Arm) has enabled on-device product recommendations that are instantly updated without the need to operate the cloud. This helped online shoppers find what they needed faster while keeping their browsing data private.
Another example comes from consumer technology: Meta’s Ray-Ban astute glasses, which combine cloud and on-device AI. The glasses support quick commands locally for faster response, while heavier tasks such as translation and visual recognition are processed in the cloud.
“Every major technological change has created new ways to engage and earn,” Bergey says. “As AI capabilities and user expectations grow, more and more intelligence will need to move closer to the edge to deliver the kind of immediacy and trust that people now expect.”
This change also affects the tools people operate every day. Assistants like Microsoft Copilot and Google Gemini combine intelligence in the cloud and on-device to bring generative AI closer to the user, providing faster, safer and more contextual experiences. The same principle applies across industries: the more intelligence you safely and efficiently bring to the edge, the more responsive, private and valuable your operations will be.
Build smarter in terms of scale
The explosion of AI on edge devices requires not only smarter chips, but also smarter infrastructure. By matching computing power to workload demand, enterprises can reduce energy consumption while maintaining high performance. This balance of sustainability and scale is quickly becoming a competitive differentiator.
“The demand for compute power, both in the cloud and on-premise, will continue to skyrocket. The question is: how do we maximize the value from this computation?” he said. “You can only achieve this by investing in computing platforms and software that scale with your AI ambitions. The true measure of progress is enterprise value creation, not raw performance metrics.”
Smart foundation
The rapid evolution of AI models, especially those that leverage power-edge inference, multimodal applications, and low-latency responses, requires not only smarter algorithms, but also a foundation of high-performance, energy-efficient hardware. As workloads become more diverse and distributed, legacy architectures designed for classic workloads are no longer appropriate.
The role of processors is evolving and they are now at the center of increasingly heterogeneous systems that deliver advanced AI experiences on devices. With their flexibility, performance, and support for mature software, newfangled processors can handle everything from classic machine learning to sophisticated generative artificial intelligence workloads. Combined with accelerators such as NPUs or GPUs, they intelligently coordinate computations throughout the system, ensuring the right workload is on the right engine for maximum performance and efficiency. The processor continues to be the foundation that enables scalable and competent AI anywhere.
Technologies such as ARM’s Scalable Matrix Extension 2 (SME2) provide advanced matrix acceleration on Armv9 processors. Meanwhile, Arm KleidiAI, the bright software layer, is broadly integrated with leading platforms to automatically improve performance for a wide range of AI workloads, from language models to speech recognition to computer vision, running on ARM-based edge devices – without the need for developers to rewrite code.
“These technologies ensure that AI frameworks can leverage the full performance of ARM-based systems without additional developer effort,” he says. “This is how we make AI both scalable and sustainable: by embedding intelligence in the foundations of modern computing, so innovation happens at the speed of software cycles, not hardware.”
This democratization of computing power will also facilitate the next wave of real-time bright solutions across the enterprise, not just for flagship products but across entire device portfolios.
The evolution of edge artificial intelligence
As AI moves from isolated pilot projects to full-scale deployments, enterprises that connect intelligence across every layer of infrastructure will succeed. This seamless integration will rely on agent-based AI systems, enabling autonomous processes that can reason, coordinate and deliver value instantly.
“The pattern is familiar because with each disruptive wave, slow incumbents risk being overtaken by new entrants,” he says. “The companies that thrive are the ones that wake up every morning asking how to make their organization AI-first. As with the rise of the Internet and cloud computing, those who lean in and truly embrace AI will shape the next decade.”
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