The Consumer Device Challenge:

Bridging hardware, intelligence, and trust.

Is on-device AI persuading PC buying behavior?

Ishan Dutt,
Research Director, PC

The AI premium remains unproven in purchase behavior

The value is still undetermined of AI on PC functionality for both the household buyer choosing at retail, and for the enterprise buyer choosing through procurement.

A recent Omdia survey revealed that consumers are willing to pay for AI to run locally rather than in the cloud. When offered two otherwise identical computers, 53% of PC buyers indicated they would pay more for on device AI processing over server based; that figures rises to 77% when you include those who prefer it without paying a premium.

How much more, if anything, would a computer that handles AI tasks directly on the device be worth of you?

How much do you agree with each statement regarding AI in PCs?

Whilst our latest Omdia data revealed a trend toward AI on device, this is yet to be translated into purchase behavior. Only 6% of buyers cite AI as the reason they bought their last PC despite 62% of them using AI weekly or more. Replacement reasons remain conventional: wanting something newer or faster, running low on storage, or the old one breaking down. AI has not yet entered that list.

No single player owns the on-device trade-offs of control, capability and cost

The gap narrows by use case, and the pattern reflects buyers weighing control, capability, and cost differently depending on context. Individuals who use their PC mainly for work are nearly twice as likely to say they would pay for on-device processing as those using it mainly for personal tasks (69% vs. 36%).

For work, control and capability outweigh the added hardware cost. For personal use, budget wins, as consumers are more likely to prioritize performance, battery life, and longevity than on-device AI processing. Enterprise procurement raises a formal version of the same question, weighing security, governance, and cost together when it evaluates AI deployment. The same trade-off between capability and control that consumers are making informally when they say they would rather their data stay on the device.

No single vendor currently owns the trade-off between cost, control, and capability: none combines all three levers needed to make the case to buyers and monetize it. Device makers hold the distribution and the brand relationship but hold it loosely: over half of Windows buyers say they are open to switching brands. AI model providers hold the capability but have no built-in position on the device itself. Chipset vendors can make the on-device claim technically true but have no direct relationship with the buyer. Platform and service providers already hold the billing relationship for AI subscriptions, but no hardware position of their own. As no player can capture the trust premium — the compounding value that comes when users grant one company permission to orchestrate their AI experience across devices and services.

Technical capability does not equal perceived value of AI-capable PCs

Omdia forecasts that AI-capable PCs will account for 6% of global shipments by the end of 2027 (source: PC Horizon Service), so the hardware transition is happening regardless, but technical capability does not equal perceived value. This overall fragmentation is what the buyer experiences, with both consumer and enterprise buyers asking the same question: what value does an AI-enabled PC actually deliver to justify a cost premium? Neither has consistently been given the practical, daily use cases that would answer it.

Note: Data in this chapter is drawn from Omdia's 2026 PC Horizon Consumer Survey (n=3,501) unless otherwise noted.

Productivity gains from AI assistants and security seen as the primary drivers for AI adoption in North America

Poll: What is the biggest barrier to AI PC adoption today?

Poll: What will be the primary driver for AI PC adoption among your customers?

Can on-device AI drive meaningful smartphone differentiation?

Amber Liu,
Practice Leader

On-device AI’s challenge to demonstrate meaningful differentiation

AI has moved from background optimization to a prominent part of smartphone product and marketing strategies. Yet consumers often struggle to distinguish between capabilities enabled by the device, operating system, cloud or a third-party app.

Omdia’s research shows that users primarily associate smartphone AI with recognizable tools such as ChatGPT and Gemini, while awareness of vendor-specific AI propositions remains limited. As similar features spread rapidly across platforms, the source of differentiation is shifting from feature availability to experiences that benefit meaningfully from deeper hardware and system integration.

Personal context is emerging as both a retention asset and a trust boundary

The value of an assistant increases as it understands a user’s preferences, routines, and activity across devices. This accumulated context can deepen ecosystem loyalty and make the assistant harder to replace. At the same time, Omdia’s research shows that privacy and security concerns remain more influential than AI capabilities in many purchasing and switching decisions.

A vendor’s ability to accumulate and leverage personal context therefore become a central source of competitive advantage, while transparency, portability and accountability shape whether that advantage strengthens trust or creates resistance.

The AI-native interface opens a new contest for smartphone user influence

The app ecosystem has defined smartphone engagement for more than a decade, giving operating systems and major apps privileged access to user attention and intent. An AI-native interface could redistribute that influence by coordinating actions across apps and services through a single assistant layer.

This transition remains uncertain because existing mobile interfaces are efficient, familiar, and reinforced by years of user behavior. The strategic opportunity lies in determining which player becomes the primary interpreter of user intent, and how value flows between the device, model, application and service layers.

Longer smartphone replacement cycles raise the threshold for AI-led upgrades

Smartphone purchasing has become increasingly driven by replacement, with many consumers keeping devices for three to five years. Omdia’s survey found that only 2.3% of respondents spontaneously mentioned AI when asked what matters most in a smartphone purchase.

Cloud-based apps also give older devices access to many of the latest AI capabilities, weakening the link between AI adoption and new hardware demand. AI may therefore have greater near-term influence on premium positioning, post-purchase engagement and future brand preference than on accelerating the mass-market replacement cycle.

What would make you consider switching smartphone brands?

How TV platform monetization replaced hardware margins?

Patrick Horner
Practice Leader

Hardware margins collapse, platform monetization takes over across the TV category

The television market is the definitive proof of concept for "hardware as a platform, not just a sale." Driven by severe compression of traditional hardware manufacturing margins, major TV original equipment manufacturers (OEMs) and retail giants have fundamentally decoupled profitability from the initial physical unit transaction. The screen is no longer a commoditized display panel; it is a high-value gatekeeper for digital real estate.

The TV ecosystem transformation is powered by sophisticated Smart TV operating system (OS) licensing models and aggressive TVOS ad monetization. Platforms are no longer chosen solely for software stability. Instead, emerging, independent Linux-based operating systems—such as V Home OS (formerly VIDAA), Titan OS, and TiVo OS—are aggressively stealing market share from legacy ecosystems like Google TV. They succeed by offering flexible, active revenue-sharing models that allow third-party TV brands to continuously monetize home-screen real estate and Free Ad-Supported Streaming TV (FAST) inventory long after the customer leaves the retail store.

Retail giants are consolidating control of the TV OS layer

The high stakes of this data and platform war are reshaping the retail landscape. A clear sign of this trend is Walmart's acquisition of Vizio and the subsequent migration of its hardware brands to VIZIO OS. This structural shift positions major retailers to control nearly half of the North American TV OS footprint. The core challenge for traditional TV manufacturers is that device distribution alone cannot sustain market leadership. As the value chain moves entirely to content platform revenue share and programmatic user viewing data, players who fail to control or partner with an agile OS layer risk losing their customer relationship to dominant software orchestrators and aggressive retail media networks.

Can AI solve the wearable interoperability crisis?

Can AI solve the wearable device interoperability crisis?

The wearable technology landscape faces a fundamental paradox: devices are becoming smarter, yet ecosystems remain fragmented. While sensors accurately capture environmental and biometric data, this information remains trapped in vendor silos—sleep data in one app, activity tracking in another, nutrition in a third. Users currently serve as the manual integration layer, bridging gaps that the architecture should handle automatically.

The standalone push

Companies are integrating AI and enhanced connectivity to grant wearables greater standalone capabilities, but their motivations diverge sharply based on their market position.

For internet giants and new market entrants without established hardware ecosystems, standalone features represent a strategic bypass around ecosystem lock-in. These players—exemplified by companies launching AI-native devices with independent eSIM connectivity—aim to create direct user relationships without depending on smartphone manufacturers' platforms. They bet that sufficiently capable standalone devices can create new entry points into users' digital lives.

Conversely, established device vendors with mature ecosystems pursue standalone capabilities for different reasons. Rather than breaking free from ecosystems, they're expanding them. Enhanced edge processing and independent connectivity enable these vendors to serve users across more scenarios—during workouts when phones are left behind, in professional settings where discreet assistance matters, or in health monitoring that requires 24/7 continuity. The goal is to cover different scenarios, ensuring the ecosystem stays relevant no matter what device the user has.

Jason Low,
Research Director

AI agents can bring significant benefits to users by enhancing the roles of different devices in the ecosystem

In both cases, AI plays a critical orchestration role. New entrants must design workflows where standalone devices deliver complete value without ecosystem support—AI becomes the primary interface and task-completion engine. Established vendors must architect how AI distributes intelligence across their ecosystem, deciding what runs locally on wearables versus centrally on phones, and where value surfaces to users. Both paths require rethinking not just device capabilities, but the fundamental workflow of how users interact with technology across contexts.