Consumer Device Strategic Implications:

From hardware sales to platform value.

Recurring PC revenue opportunities

Ishan Dutt,
Research Director, PC

Recurring revenue outpaces PC hardware premium

The AI device premium is only one part of the opportunity. As organizations start shifting to credit based charges, there is opportunity for recurring revenue with:
39% of PC users currently paying for an AI tool or feature, mostly through a subscription rather than a hardware purchase. Software and model providers are being paid for the same activity that hardware vendors still treat as an unpriced feature.

The same shift shows up in how buyers describe value. 44% already say a named AI tool such as ChatGPT, Copilot, Gemini makes their computer valuable, ahead of traditional productivity software at 39% and behind only the operating system itself. Consumers are crediting the assistant, but not the hardware underneath it.

That is the opening for default-assistant placement and co-branded features: the association buyers already make is with the software brand, and a device maker or chipset vendor outside that association is outside the value story too.

Chipset vendors must tie AI silicon to attributes that already drive purchases

For chipset vendors, the more defensible near-term position is not to sell AI capability on its own terms as the purchase data does not support that yet. Linking AI silicon to the attributes that already move a purchase decision, such as speed and battery life, where an NPU has a genuine, measurable case, will make robust market position.. But the long-term opportunity is integration, as no device maker can deliver on-device AI without silicon designed for it.

Enterprise buyers need a multi-vendor stack, not fragmented vendor pitches

Enterprise buyers add a second version of the same opportunity. Security, governance, and cost all factor into how procurement evaluates AI deployment, and no single player device maker, chipset vendor, model provider currently answers all three at once. That is where channel and systems-integration partners have room to operate, assembling a multi-vendor AI stack into something IT can approve, rather than leaving enterprise buyers to reconcile four separate pitches on their own.

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

Who controls user intent in the smartphone and the device’s response?

Amber Liu,
Practice Leader

OEM vendor strategies for controlling user intent and AI response

The “assistant moment” is the point at which a user expresses an intention, and the device determines which model, app, or service will fulfill it. Original equipment manufacturers (OEMs) are well positioned to shape this interaction through their control of the operating system, permissions, and device ecosystem while building trust with users in every small step. Vendors' strategic role is not only to provide a coherent experience enriched by third-party model and service partners, but also to give users a balanced level of comfort and control as they engage with ever-expanding intelligent features on devices. A clear role of the assistant with a technical capability boundary will help users connect the assistant’s value with the device brand, strengthening retention without requiring OEMs to develop every underlying capability.

Chipset players’ role in enabling trusted and always-on smartphone AI use cases

Always-on AI shifts the chipset proposition beyond peak processing performance. Continuous awareness of user context requires efficient processing across the CPU, GPU and NPU while operating within battery, thermal and memory constraints. Chipset differentiation will increasingly depend on enabling AI that responds quickly, protects sensitive information locally and moves smoothly between device and cloud.

Closer alignment with OEM and model roadmaps can also reduce fragmentation and shorten the path from new model capabilities to widely available smartphone experiences. Chipset providers provide the technical foundation for trust by enabling reliable, privacy-preserving AI across everyday use.

AI model providers poised to establish the terms of scalable AI partnerships

AI model providers bring rapidly evolving capabilities and stronger consumer recognition than device brands. Sustainable partnerships require commercial and governance terms that reflect each participant’s contribution, while allowing AI model providers to adapt to different device OEM requirements for security, permissions and data handling. Revenue-sharing structures can account for distribution, user acquisition, inference costs and downstream transactions.

Data rights need equal clarity, covering user consent, permitted access, model training, portability and responsibility when the assistant produces an incorrect outcome. These agreements will determine whether AI model partnerships create durable differentiation or only temporary feature parity.

"What limits your usage of AI features on your smartphone?”

Service providers monetize trust through cross-device and cross-service continuity

Operators, cloud platforms and other B2B2C providers can build monetization layers around recurring user needs rather than charging for AI as a standalone feature. Paid tiers may offer higher usage allowances, advanced capabilities or services spanning productivity, entertainment, security and care.

Cross-device continuity can increase the value of these subscriptions by allowing personal context and service access to follow the user across smartphones, PCs, wearables, hearables and the connected home. Subscription and cross-device services can convert trust into recurring value, but sustaining that trust requires transparency around the use of personal data, commercial partnerships and how these relationships may influence AI recommendations.

Why the TV playbook matters for every hardware category?

Patrick Horner
Practice Leader

Strategic recommendations for the connected TV ecosystem

The TV playbook provides a critical strategic blueprint for the broader hardware ecosystem, offering vital lessons for PC, smartphone, and smart home OEMs on strategic sequencing. To defend and extend their monetization lead as AI-native experiences—such as generative discovery, intelligent voice interfaces, and hyper-personalized content recommendations—become standard, TV makers must avoid a common trap. They cannot let third-party AI models commoditize the underlying operating system. OEMs must protect the user touchpoint. They should treat the incoming AI layer as a plug-and-play feature that enhances engagement rather than an external takeover of the screen's ad inventory.

AI model providers can plug into proven monetization structures without building hardware

This environment creates massive commercial openings for AI model providers and B2B2C service providers. Rather than trying to build competitive hardware networks or independent operating systems from scratch, AI model providers can seamlessly plug into already-proven monetization architectures. A premier example of this is the high-profile integration of Microsoft Copilot into V Home operating system (OS), which directly embeds generative intelligence into an active consumer platform without disrupting the underlying ad-supported revenue split.

The sequence matters: hardware trust, platform monetization, then AI partnerships for the TV industry

Other consumer electronics categories must implement this same sequence. Build hardware trust, secure the platform footprint via robust monetization models, and only then negotiate with AI model providers entities from a position of distributional strength.

Who will control the cross-device orchestration layer?

Jason Low,
Research Director

The stakes of AI-driven interoperability: Why cross-device fragmentation threatens the AI revolution

The fragmentation crisis in wearables and connected devices represents a fundamental barrier to AI's potential in daily life. Without interoperability standards, users remain trapped in ecosystem silos, unable to access the full value of their data. But fragmentation runs deeper than hardware: app store policies, API restrictions, and platform politics create software-level barriers that prevent AI agents from orchestrating across services. When an AI agent can't book a ride because it lacks access to competing transportation apps or can't complete a purchase because payment APIs are gated, the promise of seamless task completion collapses.

The company or consortium that establishes the dominant interoperability framework—spanning both hardware and service access—will control the most valuable layer of the AI stack: the orchestration point where user intent meets cross-device, cross-service execution.

Trust-sensitive data requires privacy-preserving AI architectures

This control point is particularly critical because it sits atop the most trust-sensitive data streams in consumer technology. Health metrics, productivity patterns, and home behavior represent intimate insights into users' lives. Any player seeking to orchestrate across these domains must first solve the trust equation—demonstrating not just technical capability, but transparent data governance and reliable task completion. Privacy-preserving AI architectures that process sensitive data locally while enabling cross-device coordination will become competitive differentiators.

For AI companies, this device and service fragmentation creates a strategic opening. Rather than competing directly with hardware ecosystems, they can position themselves as the "connective intelligence" layer, the neutral orchestration platform that makes fragmented devices and services work together. This B2B2C model offers clear monetization paths: subscription services that solve real consumer pain points (unified health insights, seamless productivity workflows, intelligent home automation) while giving hardware vendors differentiated AI capabilities they can't build alone.

Device vendors poised to succeed will be those that establish a trusted, interoperable intelligence layer across all devices

The winners in this emerging market for AI-powered device and service integration won't be determined by who builds the best standalone device or the largest ecosystem, but by who establishes the trusted, interoperable intelligence layer that makes all devices—regardless of vendor—work together seamlessly. That's where the real value lies, and where the market will ultimately consolidate.