February 05, 2026
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How AI PCs can deliver measurable ROI in the new year ahead, based on findings from the 2025 Forrester New Technology Total Economic Impact™ Study of Microsoft Copilot+ PCs
Budget planning in the new year increasingly requires defensible data. As organizations set priorities and allocate IT spend, a familiar tension often emerges: leadership expects AI-driven productivity gains, but many device fleets weren't originally designed to support AI workloads reliably or at scale.
A July 2025 Forrester New Technology Total Economic Impact™ (NTTEI) study, commissioned by Microsoft, examined outcomes reported by organizations that refreshed aging hardware with Microsoft AI PCs—devices designed to support on-device AI compute. To better understand potential benefits, costs, and risks, Forrester interviewed decision-makers with experience using Copilot+ PCs and aggregated their experiences into a single composite organization (2,000 employees; ~$1B annual revenue). Based on this composite model, the study projected a 137%–367% ROI over three years and $2.9M–$7.7M in net present value, with value attributed to reported gains in end-user productivity, IT efficiency, and security risk reduction.
The study suggests that information technology budget decisions are increasingly influenced by infrastructure readiness, not AI ambition alone. Delaying modernization may introduce additional planning complexity and can slow AI adoption as organizations enter a new budgeting cycle.
Why aging devices can contribute to operational drag
According to Forrester’s analysis, participants reported recurring challenges that often surface during planning cycles, including slower workflows, higher support effort, and increased operational overhead:
Workflows strain under load.
AI productivity tools like advanced search, automated tasks, and multi-app workflows can push older devices past their compute and memory limits. For mobile roles, increased battery drain may further compound these challenges.
Support demand increases.
VPN instability, camera failures, driver conflicts, and performance-related tickets can consume IT capacity that might otherwise be directed toward modernization efforts. Some organizations reported up to 30% fewer device-related tickets after refreshing hardware.
Security risk exposure rises.
Aging hardware may lack certain chip-level protections, which can increase audit complexity and incident-response overhead. These are costs that have the potential to scale quickly across the fleet.
AI pilots face adoption friction.
Devices may struggle to support AI workloads consistently enough for users to experience sustained value, potentially slowing adoption before AI tools can deliver returns. Failed pilots can also influence leadership confidence and future funding decisions.
Over time, device age itself can become a systemic constraint—one that sharpens during annual reporting periods and operational peaks.
What organizations reported improving with hardware built for AI workloads
Forrester’s study indicates that devices designed to support local AI compute can help reduce certain operational friction by shifting AI processing away from overburdened CPUs/GPUs, while keeping some workloads on-device. Participants in the study reported several areas of improvement:
Performance stabilizes.
Organizations noted faster app launches, smoother multitasking, more reliable offline work, and longer unplugged run times.
Search and retrieval accelerate.
Some participants reported faster and more accurate information discovery, including in offline scenarios, and particularly in environments with large data volumes.
Collaboration becomes predictable.
Video, audio, and connectivity were reported as more reliable under load, helping reduce friction in hybrid work scenarios.
IT efficiency increases.
Provisioning time was reported to decrease from 45–60 minutes to ~15 in the composite model. Device management time declined by 20–30%, while OS- and hardware-related tickets declined 10–40% in Year 1 and up to 50% in Year 2.
Security strengthens at the silicon level.
The NTTEI model attributes $22K–$66K in avoided costs over three years to hardware-backed isolation features, based on reduced breach exposure assumptions within the composite organization.
Collectively, these reported improvements represent reclaimed operational capacity, which can help IT leaders plan more predictably for the year ahead.
Where AI PC ROI is generated, according to the NTTEI model
The Forrester NTTEI model quantifies projected value across three areas relevant to IT planning and investment discussions:
Across all three categories, the study associates value with reduced friction: lower latency, fewer bottlenecks, less manual remediation, and more predictable performance across the fleet.
A phased refresh structure aligned to IT planning cycles
The study indicates that organizations reporting the strongest returns adopted phased refresh approaches aligned to common IT planning practices:
Year 1
Deploy ~800 AI PCs to engineering, analytics, consulting, sales, and compute-intensive roles. The model projects 1.5–2.5 reclaimed hours weekly and 10–40% fewer support tickets.
Year 2
Deploy ~400 devices to secondary teams. Projected power-user efficiency rises to 2–3.75 hours weekly.
Year 3
Deploy the remaining ~400 devices. Projected efficiency for power users reaches 2.5–5 hours weekly, with 1.5-3 hours for general users.
Participants aligned refresh waves to budget windows, smoothed capital outlay, and used early efficiency signals to inform continued investment decisions.
Four indicators your organization may be ready for AI PCs
Based on the NTTEI findings, organizations may see more meaningful returns when several of the following conditions are present:
These indicators align with broader business-readiness considerations, including regulatory exposure, rising ticket volume, and latency during critical operational cycles.
Infrastructure readiness influences AI readiness
AI adoption, security posture, and operational resilience are closely tied to whether devices can run modern workloads reliably. When hardware reduces friction instead of contributing to it, IT may gain the bandwidth to modernize systems, deliver new capabilities, and scale AI more securely.
Pushing hardware refreshes further into the new year can stall AI ROI and may increase support burden. As reflected in the NTTEI findings, device refresh decisions increasingly function as a foundational element of an organization’s AI roadmap, not just a lifecycle exercise.
Ready to modernize your device strategy?
For organizations evaluating modernization plans, the Forrester NTTEI findings provide a structured, data-backed framework for assessing when AI-capable hardware may warrant prioritization.
Your next device refresh doesn't have to be solely maintenance-driven. It can be the foundation that enables AI adoption, strengthens security, and reduces operational drag across the organization.
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