Presented by Arm
AI is no longer limited to the cloud or data centers. Increasingly, this is playing out directly where data is created – in devices, sensors and networks at the edge. This shift toward on-device intelligence is driven by latency, privacy and cost concerns that companies are grappling with as they continue to invest in AI.
For leadership teams, the opportunity is clear: invest in an AI-first platform that complements cloud usage, delivers real-time responses, and protects sensitive data, says Chris Bergey, SVP of client business at ARM and GM.
"With the explosion of connected devices and the rise of IoT, AAI offers organizations a significant opportunity to gain a competitive edge through faster, more efficient AI," Burji explains. "Those who move first aren’t just improving performance, they’re defining customer expectation. AI is becoming a differentiator in trust, responsiveness and innovation. The sooner a business centralizes AI into its workflows, the faster it reaps the benefits."
Use Cases: Deploying AI Where the Data Lives
Enterprises are discovering that AAI isn’t just an efficiency boost—it’s a new operational model. Processing locally means less reliance on the cloud and faster, safer decision-making in real time.
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 deploying in-store analytics using vision systems while logistics companies are using on-device AI to improve fleet operations.
Instead of sending vast volumes of data to the cloud, organizations can analyze and act on insights where they emerge. The result is a more responsive, privacy-preserving and cost-effective AI architecture.
Consumer Expectations: Reinforcement and Trust
Working with Alibaba’s Taobao team, the largest Chinese e-commerce platform, ARM (Nasdaq: ARM) enables on-device product recommendations that update instantly without relying on the cloud. This helped online shoppers find what they needed faster while browsing the data.
Another example comes from consumer tech: Meta’s Ray-Ban smart glasses, which combine cloud and on-device AI. Glasses handle immediate commands locally for fast response, while heavy tasks like translation and visual recognition are processed in the cloud.
"Every major technology shift has created new ways to engage and monetize," Barji says. "As AI capabilities and user expectations grow, more intelligence will need to move closer to the edge to deliver the kind of empowerment and trust that people now expect."
This change is also happening with the tools that people use every day. Assistants like Microsoft Copilot and Google Gemini are combining cloud and on-device intelligence to bring generative AI closer to the user, delivering faster, more secure and context-aware experiences. The same principle applies in industries: the more intelligence you can safely and efficiently move to the edge, the more responsive, private and valuable your operations become.
Smart building for scale
The explosion of AI at the edge demands not only smarter chips, but smarter infrastructure. By aligning compute power with workload requirements, enterprises can reduce energy consumption while maintaining high performance. This balance of stability and scale is becoming a competitive differentiator.
"Compute needs, whether in the cloud or on premises, will continue to grow exponentially. The question becomes, how do you get the most value out of this compute?" He said. "You can only do this by investing in compute platforms and software that scale with your AI ambitions. The real measure of progress is enterprise value creation, not raw performance measurement."
Intelligent Foundation
The rapid evolution of AI models, especially those involving power acquisition enhancements, multimodal applications, and low-latency responses, demand not only smarter algorithms, but also a high-performance, energy-efficient hardware foundation. As workloads become more diverse and distributed, legacy architectures designed for traditional workloads are no longer sufficient.
The role of CPUs is evolving, and they now sit at the heart of increasingly heterogeneous systems that deliver sophisticated AI experiences. Thanks to their flexibility, performance, and mature software support, modern CPUs can run everything from classical machine learning to complex generative AI workloads. When paired with accelerators like NPUs or GPUs, they intelligently coordinate compute across the entire system – running the right workload on the right engine for maximum efficiency and performance. The CPU is the foundation that enables scalable, efficient AI everywhere.
Technologies such as ARM’s Scalable Matrix Extension 2 (SME2) bring advanced matrix acceleration to the ARM V9 CPU. Meanwhile, its intelligent software layer, ArmClydia, has been widely integrated to boost the performance of a wide range of AI workloads, from language models to speech recognition to computer vision, running on Arm-based edge devices—without requiring developers to rewrite their code.
"These technologies ensure that AI frameworks can be incorporated into the full performance of arm-based systems without additional developer effort." He says. "This is how we make AI scalable and sustainable: by embedding intelligence into the foundation of modern computing, so innovation happens at the speed of software, not hardware cycles."
The democratization of compute power is also what will facilitate the next wave of intelligent, real-time experiences across the enterprise, not just in flagship products, but across device portfolios.
Evolution of Edge AI
As AI moves from isolated pilots to full-scale deployment, enterprises that succeed will be those that integrate intelligence into every layer of infrastructure. Agentic AI systems will depend on this seamless integration – enabling autonomous processes that can reason, coordinate and deliver value instantly.
"The pattern is as familiar as any disruptive wave, with incoming individuals gradually threatened by newcomers," He says. "The companies that thrive will be the people who wake up every morning asking how to build their organization in the first place. As with the rise of the Internet and cloud computing, those who are bent and truly AI-enabled will shape the next decade."
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