AI workloads are also moving beyond the cloud. While centralized AI remains critical, cloud-only processing introduces constraints around latency, data privacy, and cost predictability at scale. Deloitte notes that even as AI processing costs decline significantly, enterprise spending continues to rise as usage expands, pushing many organizations toward hybrid models that balance cloud and on-device AI.

Together, these trends are changing how business PCs are designed, secured, and refreshed. This guide explains what defines an AI PC and provides a practical framework for assessing where, why, and how to deploy them at scale.

What Is an AI PC?

An AI PC is a business-class computer designed to run AI-powered features and applications directly on the device instead of solely through cloud services. As AI becomes embedded into productivity software, collaboration tools, and operating systems, the PC itself is evolving from a passive access point into an active participant in how work gets done.

When supported, built-in AI acceleration allows features such as live transcription, background noise suppression, image enhancement, and contextual assistance to operate continuously with less risk of overloading the system or draining battery life. Rather than treating AI as an add-on, AI PCs are designed with AI processing as part of the core hardware platform.

The transition is driven by both software platforms and hardware manufacturers. Microsoft is integrating AI features directly into Windows and Microsoft 365, and chipmakers and PC vendors are embedding NPUs into next-generation business devices to support those experiences, quickly moving AI acceleration from a premium capability to a baseline expectation in commercial PC design.

Traditional Enterprise PC vs. AI PC

‘AI-ready’ means more than supporting a few AI features. It means the device can run approved AI experiences reliably and securely at scale, with clear expectations for performance, privacy, and manageability.

Why NPUs Matter for Performance and Battery Efficiency

For enterprise IT teams, the NPU is the most significant architectural addition.

Unlike CPUs and GPUs, which are designed for broad compute performance, NPUs are optimized for AI-related operations and high efficiency per watt so always-on AI features run in the background with less impact on system responsiveness and power draw.

For mobile workforces, this translates into:

• More consistent collaboration performance
• Reduced thermal strain
• Better battery outcomes during AI-heavy workflows

Memory and storage matter too. For AI-enabled workflows, plan for RAM and SSD headroom, because AI features add background load and can increase local storage use.

AI PC Form Factors and Deployment Scenarios

AI PCs vary by form factor, and the right choice depends on workload intensity, mobility requirements, and how consistently devices need to be standardized across a fleet.

• Ultraportables and standard business laptops: Prioritize efficiency and collaboration performance for everyday productivity, especially for AI-enabled meeting and communication features
• Mobile workstations: For engineering, creative, and data-heavy users, pair an NPU with a discrete GPU for sustained performance
• Standardized mobile fleets. Focus on a small number of repeatable configurations that simplify device setup, support, lifecycle planning, and procurement while meeting baseline AI-readiness needs
• Specialty and rugged devices: For frontline and industrial environments, durability and faster response time can matter more than maximum performance, especially for vision and real-time analysis use cases

Business Use Cases Driving AI PC Adoption

AI PCs matter most when people need help in the moment, when information is sensitive, or when the same tasks happen repeatedly throughout the day. The examples below show where on-device AI can improve responsiveness, reduce how often work needs to be sent to the cloud, and make every day work smoother.

Smarter Collaboration with NPUs

When collaboration apps support the NPU, features like transcription, live captions, translation, and meeting summaries can run with less impact on the CPU and battery. Some processing can also stay on the device, which can reduce data exposure for supported features.

Everyday Workflow Automation and Contextual Search

AI PCs can support on-device AI agents for some tasks like drafting, summarizing, scheduling, and creating routine updates, which can improve speed and responsiveness.

Why On-device AI Changes the Security Conversation

On-device AI moves more processing from cloud services to the device, which can reduce how much sensitive data is sent off-device and improve reliability in low-connectivity environments. It also expands what must be protected locally, including AI models and access to AI-powered tools, increasing the importance of device-level protection and sign-in requirements.

Running AI locally supports privacy and compliance by keeping some processing on the device. At the same time, decentralizing AI introduces new challenges, including unmanaged AI tools, data leakage, and higher risk if compromised devices have access to critical systems.

How to Align AI PCs with Security Strategy

Before rolling out AI PCs at scale, use this checklist to confirm access, monitoring, and auditing are in place for on-device AI features.
✔  Allow only approved local AI tools and models on managed devices
✔  Require sign-in and policy controls before AI tools can take actions
✔  Give AI tools only the access they need, and remove access when it’s no longer needed
✔  Monitor AI tool use in your existing device security monitoring
✔  Record AI-driven actions so they can be reviewed and audited

Managing AI PCs Across the Enterprise

AI PCs’ on-device AI capabilities change how IT plans hardware, software, security, and cost. The goal is to support these devices at scale without adding new complexity to imaging, updates, and support.

Assessing Organizational Readiness

Before investing, confirm where AI-capable hardware improves real work outcomes. Start by identifying the apps and tasks that benefit most from local AI features, and where cloud AI is still the better fit. Then prioritize roles where those features are used daily, so the first deployments deliver measurable value.

Compatibility with Device Management Platforms

AI PCs should fit into your existing management stack, including Microsoft Intune and Microsoft Configuration Manager (MECM).

Prioritize models that support your standard device setup, policy enforcement, and update processes—including OS, drivers, and firmware—so AI features don’t become an unmanaged exception.

Lifecycle and Complexity Management

To avoid complexity, standardize on a small number of device tiers and retire overlapping tools as AI features roll out. Define which AI features are allowed, how they’re configured, and how activity is monitored.

Treat AI-related updates like any other change: test, deploy in phases, and review impact on support tickets and performance.

AI PC Purchasing Considerations for Enterprise Buyers

Buying AI PCs is not a routine refresh. AI features will evolve faster than your hardware cycle, so the goal is to standardize a small set of configurations that stay useful for the next 3–5 years without increasing support complexity.

Building an AI PC Adoption Roadmap

Procurement determines what you buy, while adoption determines whether AI PCs deliver value in day-to-day work. This roadmap connects roles, controls, standards, and measurement so rollout stays practical at scale.

AI PCs Are Becoming the New Standard

Many business tools now include AI features, changing what work laptops need to handle. Laptops with a built-in NPU can run some of these features on the device to help improve responsiveness and keep some sensitive work from being sent off the laptop.

New hardware alone won’t deliver results. Value comes from choosing the right devices for the right roles, setting clear rules for which AI features are allowed and where data is handled, and rolling out in stages so support and security stay manageable.

Connection is a trusted partner that can help assess readiness, standardize configurations, and buy and deploy business laptops built for on-device AI.