There is a spring in this old computer marketeer’s step because, in 2026, I think artificial intelligence (AI) of all kinds, is poised to enter a phase where growth is not just driven by cloud models and enterprise applications, but by widespread consumerisation, integration into physical devices, and a renaissance in computer hardware development. These three vectors are converging precisely now because of shifting market dynamics and technological maturity.
Firstly, consumerisation is transforming how AI technologies are adopted. Over the past several years, AI capabilities—such as natural language understanding, real-time translation, and generative content creation—were mostly confined to desktop applications or cloud services used by businesses. But, in 2026, we are entering an era where these capabilities are becoming deeply embedded in everyday consumer experiences. Voice assistants are becoming more sophisticated, on-device AI is standard on smartphones, and consumer apps now rely on AI for everything from immersive Augmented Reality (AR) to personalised health insights. Consumers are no longer passive users; they actively expect smart, adaptive, and predictive functions in the products they buy.
This change is not simply cultural—it’s economic. Device manufacturers see clear value in differentiating through intelligent features, and consumers are willing to pay premiums for devices that offer genuinely useful AI. This creates a feedback loop: as more consumers use AI-enabled products, demand grows for even more advanced and responsive models tailored to personal use.
Secondly, physical devices are rapidly becoming the primary interface for AI interaction. AI is moving out of the cloud and into the “edge” of the network—meaning smart sensors, wearables, robots, vehicles, and other physical endpoints. This trend is fuelled by the limitations of remote processing: latency, privacy concerns, and bandwidth costs. Users want instant responses and secure processing of personal data. For example, AI in wearables can monitor health signals in real time; in smart home devices, it can optimise energy use and anticipate needs; and in autonomous systems, such as delivery robots or drones, it must make decisions locally without connectivity delays.
As more hardware incorporates AI cores and accelerators, these devices become platforms for continuous learning and adaptation, enhancing user experiences and enabling new use cases. The physical world becomes the AI playground, and innovations are judged not only by accuracy but by real-world responsiveness.
Finally, computer hardware is essential to sustaining accelerated AI growth. Advances in specialised processors, such as AI accelerators, neuromorphic chips, and quantum co-processors, are unlocking performance gains that cloud-only infrastructures can’t match. In 2026, hardware development is no longer just about higher clock speeds or more cores; it is about efficiency, parallelism, and real-time inference. This shift matters because AI workloads, especially generative and multimodal models, require enormous processing power. Companies that deliver chips optimised for AI tasks are enabling devices to run sophisticated models locally, vastly reducing reliance on remote data centres.
Moreover, the semiconductor industry is responding to these demands with renewed investment in lithography, memory technologies, and chip design tailored for AI. This hardware evolution is critical: without it, the consumer-facing and device-embedded AI experiences of 2026 wouldn’t be feasible.
In summary, 2026 marks a turning point where AI growth hinges on making intelligence personal (consumerisation), bringing it into the physical world (devices), and equipping it with the performance it needs (hardware). These forces together redefine how AI impacts our daily lives, turning it from a cloud-centric service into a pervasive, embedded, and indispensable part of the human experience.
by Paul Bevan – Tech Industry Forum STAR Group advisory council member
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