"Physical AI is most often used to refer to robots, but it increasingly encompasses every category of AI-enabled device that is already in use today – our phones, AI-enabled PCs, smart speakers, fitness trackers, Agentic glasses, and AI-enabled cars – and connects us to a digital assistant, an AI agent, or any kind of AI-enabled feature. But to drive demand at scale, this interconnected ecosystem of devices needs to start solving real problems for real people, or demand may soften.”

Olivier Blanchard

Research Director & Practice Lead, Intelligent Devices

ROI, Security, Efficiency, and UX Will Continue to Drive Expansion of AI Capability to the Edge Beyond 2026

High-performance, power-efficient AI-capable silicon will continue to enable increasingly sophisticated AI use cases at the edge, accelerating the expansion of AI workloads into edge form factors such as devices, vehicles, IoT, and other physical AI. These edge AI use cases will increasingly complement cloud-based AI functions in an orchestrated, distributed hierarchy of AI workloads.

While edge AI is not new, 2026 product launches highlight rapid innovation and an expanding variety of AI-capable form factors.

  • AI-enabled devices, including mobile phones, PCs, and wearables like smart glasses, are rapidly evolving, offering advanced assistant, productivity, and security features. These devices are increasingly mainstream, combining context-aware sensing, predictive analysis, and hands-free voice control for everyday tasks.
  • Smart appliances, such as AI-powered TVs, now offer seamless natural-language control. Simultaneously, AI cameras are advancing in precision autonomous recognition, while drones and semi-autonomous vehicles are becoming increasingly context-aware, enabling sophisticated real-time decision-making.
  • AI-enabled vehicles continue to drive towards not only full autonomous driving, overall performance optimization, and maintenance management expectations, but also next-generation agentic in-vehicle experiences.
  • The robotics race is on in earnest, and we expect investments in physical AI to ramp up between now and 2030, with every major technology company already driving the AI ecosystem, looking to compete in this still-nascent segment.

Device and Semiconductor Vendors’ Commitment to Edge AI Expansion: Major silicon and device vendors—from established leaders like Qualcomm, Apple, and NVIDIA to players like Amazon and Google—are aggressively competing to scale edge AI in IoT, automotive, and robotics. In 2026, partnerships across semiconductors, hardware, and AI platforms will be essential to consolidate ecosystems focused on interoperability and performance.

  • Moving AI Processing from the Cloud to Devices: While AI training continues to be primarily cloud-based, particularly for LLMs (large language models), smaller models can increasingly be trained on devices for highly specialized or specific use cases. 
  • From Training to Inference: Edge inference is by far the biggest opportunity for AI-enabled devices and physical AI. Enabling real-time, natural-language interactions between users and devices is a primary driver of this disruption. Ensuring productivity and UX continuity regardless of network bottlenecks and connectivity challenges is a close second, with security and privacy advantages rounding out the Top 3 list of value propositions.
  • Agentic AI at the Edge: As agentic AI begins to transform the way users interact with apps and software, AI-capable devices are uniquely positioned to deliver agentic-AI-forward experiences for users – to save them time, improve productivity, and create remarkable experiences that raise the utility of and demand for every AI-enabled device category. 
  • Awareness, Context, and Agency: Devices and robots that are increasingly capable of understanding their surroundings and contextual cues will be better equipped to make useful decisions for their users or on their own. 
  • Beware the anti-AI backlash: While technology users enjoy many of the benefits of AI-enabled devices and vehicles, a growing number of them are becoming weary of AI’s potential negative impacts. Concerns range from trust, privacy, and choice to costs and negative environmental impacts. Vendors should lean on trust, privacy, and consent as critical competitive differentiators.

Research Director Olivier Blanchard covers Intelligent Devices for Futurum. In addition to having co-authored several books about digital transformation and AI with Futurum Group CEO Daniel Newman, Blanchard brings considerable experience demystifying new and emerging technologies, advising clients on how best to future-proof their organizations, and helping maximize the positive impacts of technology disruption while mitigating their potentially negative effects. Follow his extended analysis on X and LinkedIn.

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