The AI PC Question Midmarket CIOs Need to Ask

AI PC adoption is accelerating, but the real issue isn't whether employees use AI. It's whether their workloads require AI processing on the device or can continue running in the cloud.

AI PCs are moving rapidly from novelty to mainstream business hardware. Fortune Business Insights projects the global AI PC market to reach approximately $132 billion by the end of 2026, while AMD found that about 60 percent of organizations already have them in either pilot or deployment stage.

Given these figures, there is clearly an appetite for AI PCs, but adoption figures alone tell us little about whether they make investment sense for most businesses.

In the midmarket space, where IT teams face rising hardware costs and competing budget priorities, the critical question for midmarket IT leaders considering AI PCs isn't whether employees are using AI. It's whether their work requires AI processing to happen locally. For many workers, most AI tools currently still primarily rely on cloud infrastructure.

What’s Behind the AI PC Label?

The biggest hardware change inside an AI PC is the Neural Processing Unit (NPU) — a specialized processor designed to accelerate AI and machine learning tasks on the device, using far less power than a CPU or GPU.

Microsoft's Copilot+ AI PC specification, for example, requires an NPU capable of more than 40 trillion operations per second (TOPS). According to the Windows maker, the chip is designed to handle AI-intensive tasks such as speech recognition, image processing and translation while leaving the CPU and GPU available for other tasks. That sounds impressive on a specification sheet, but the more revealing question is what businesses are doing with the capability. A recent IDC white paper reveals that among organizations already deploying or piloting AI PCs, 71 percent use them for drafting documents or presentations, 68 percent for local data or spreadsheet analysis and 62 percent for meeting transcription and summarization.

Beyond those generative use cases, there are more computationally demanding applications emerging as well. Dell posted a case study on its site demonstrating how Deloitte's development teams are using AI PCs to run large language models, computer vision and generative AI directly on the device. The computer equipment manufacturer claims this has helped cut proof-of-concept timelines by up to 50 percent, while reducing reliance on cloud infrastructure and keeping sensitive data on-device.

While all these benefits matter for midmarket buyers, an AI PC may not automatically make every employee more productive. Its usefulness, according to Kirill Meshyk, head of AI data collection at Unidata, a data collection and annotation company, depends on applications being designed to exploit local processing, and many of the AI assistants employees already use continue to rely on cloud infrastructure.

Is the AI PC Premium Worth Paying?

The economics of a potential upgrade to AI PCs are becoming less favorable at the point when vendors are pushing AI PCs into corporate refresh cycles.

Gartner forecasts that combined DRAM and SSD prices could rise 130 percent by the end of 2026, potentially pushing average PC prices 17 percent above 2025 levels. IDC also forecast more than an 18 percent increase in average PC selling prices this year, with the memory shortage expected to persist into 2027.

For midmarket CIOs, that creates a more complicated purchasing decision. A company replacing 500 or 1,000 devices could be committing a substantial premium across its workforce, even though the majority of employees may spend their working day in browsers, email, documents, video calls and cloud applications — tasks that can be handled effectively by conventional business PCs.

The economics become even harder to justify when the headline AI features are still largely dependent on software and cloud services. An NPU sitting inside every laptop does little for an employee whose workload rarely calls upon it.

The strongest business case may be for selective deployment. The distinction is not between employees who use AI and those who do not. It's between employees who consume AI services and those who need to perform AI computation locally.

Who Actually Needs an AI PC?

Meshyk argues that most employees have little reason to need additional processing capability.

“For most employees, the NPU in the AI PC is practically worthless,” he told MES Computing, pointing out that emails, documents, browsers and video calls can be handled by conventional business PCs, while many of the AI assistants that employees interact with are hosted in the cloud.

The calculation changes for employees whose work requires AI computation to happen locally. Meshyk identifies use cases such as running inference against sensitive data that cannot be uploaded to the cloud, processing images and audio in real time, and carrying out local labelling or transcription. Here, the benefits of local processing become considerably more tangible. Keeping data on the device can help address privacy and compliance requirements, while eliminating the round trip to a cloud service can reduce latency for workloads that need immediate results.

Meshyk recommends that midmarket IT directors planning a refresh should first identify the employees whose workloads genuinely require local AI computation and prioritize AI PCs for those users first. The wider workforce can remain on the conventional refresh cycle while the hardware matures, and the price premium comes down in a year or so.