Enterprises aren’t just adding AI to a few workflows in isolation anymore. They’re operationalizing it at scale, and that’s changing where work happens and how fast expectations evolve. Mission-critical productivity and security functions that once lived in the cloud are now moving to endpoints, alongside security tooling that has to keep pace with an expanding range of data inputs.
Enter the AI PC. While the term lacks a single, universally accepted definition, it broadly refers to any laptop or desktop capable of running AI workloads on-device via a neural processing unit. These are usually integrated with the CPU, although they also can be discrete add-in cards for high-end workstations. Recent studies estimate that AI PCs will reach a market share of 55% in 2026, up from 31% the previous year.
These computers are designed for local processing of AI workloads, such as on-device inference, real-time data analysis and next-generation security systems. AI PCs give organizations greater control and autonomy: Because data doesn’t need to be sent off-device for processing, it reduces exposure from data movement for select workloads, which addresses concerns around data residency, sovereignty, privacy and compliance.
In many use cases, performance improves, too. For example, some AI workloads, such as AI-powered threat detection or antifraud systems, have to work in real time to be useful, so they’re extremely sensitive to latency. By running these workloads on-device, enterprises can cut out the cloud round trips that leave them at the mercy of network conditions.
Why AI requires modern device fleets
These benefits only materialize when a device fleet is modern enough to run such workloads locally, and that’s where device refresh becomes a strategic concern. “Every one of these new AI workflows introduces another agent into the mix when it comes to security. If we’re able to utilize these modern endpoints, where the data is all local instead of in the cloud, we can cut out that leg of the risk journey,” said Adam Reiser, associate vice president of modern workplace at SHI.
AI-capable endpoints also tend to incorporate the latest hardware-based security advancements, though capabilities vary between OEM and platform. For example, Microsoft’s Copilot+ AI PCs are equipped with Microsoft Pluton, a hardware-based security subsystem built directly into the CPU die designed to protect cryptographic keys from certain physical attacks. Similarly, Intel’s vPro, commonly included in enterprise-grade AI PCs, features hardware-level protections and manageability.
Naturally, IT leaders might have concerns, such as whether new devices will be compatible with legacy applications or whether adding AI capabilities on-device will really deliver measurable ROI. But leaders aren’t just weighing features and limitations anymore — they’re also trying to figure out who should get upgraded first and what signals should trigger replacement. After all, there’s more than enough hype in the market to raise important questions and objections.
That’s why Reiser advocates for an intelligent device-refresh model. “Enterprises need a data-driven approach at every step of the way, from first selecting their AI PCs to deploying and managing them to finally turning over into the next generation. It’s about doing it the right way the first time and then maximizing the potential of these new AI-ready devices throughout their life cycles.”
AI PCs are still PCs, but the bar is higher
This doesn’t suggest that PCs have become something else. AI PCs are still PCs, albeit ones with extra capabilities. However, operating systems and apps increasingly assume local AI capabilities. Current operating systems, such as Windows 11, are also designed to make these capabilities additive rather than disruptive, whereas Windows 10 — no longer supported as of October 2025 — firmly belongs to the pre-AI era.
“Now that these devices are powerful enough to perform AI tasks locally, you gain access to enhanced features from a productivity and security standpoint,” Reiser said. “It can also reduce cloud-computing costs because you can shift workflows over to local devices that are far more energy-efficient than the massive GPU clusters many enterprises are relying on today.”
None of this is to say that AI PCs are a fix-all solution. They’re well-suited to running small- to medium-size language models, but they won’t replace large-scale GPU clusters any time soon for frontier-scale training or heavy centralized inference. That’s why enterprises also need disaggregated infrastructure to manage the back end with flexible, full-stack composability.
The main value of AI PCs is how they shift many everyday AI workloads closer to the user. By reducing unnecessary data movement, they can narrow the risk journey for select workloads, but that benefit depends on disciplined device life cycle management. Devices that are just “good enough” only fall behind, and that doesn’t just slow down end users — it also adds risk.
