IT leaders are charged with optimizing enterprise IT infrastructure and delivering cost efficiencies. AI PCs and workstations can play a key role in this balancing act, especially as enterprises map out a device refresh cycle for AI.
Managing AI deployment expenses is proving to be a significant challenge, fueled by skyrocketing cloud and token costs. According to CIO.com, most organizations miss AI cost forecasts, with nearly one-quarter of IT leaders going over budget by more than 50%. What’s more, Gartner predicts AI coding costs will surpass average developers’ salaries by 2028 as token consumption surges.
In the face of cost pressures, many IT leaders still cling to one simple metric to guide refresh decisions: device age. According to research from Scalable Software, 77% of organizations replace technology on fixed refresh cycles driven by calendar schedules instead of tangible business requirements or device performance.
This age-centric approach, which typically operates on a 3-to-5-year replacement cycle, can simplify budgeting by making device costs predictable. But the reality is devices that are well past their prime can hamper an enterprise’s ability to fully capitalize on AI and all its benefits. According to research from HP, only around half of knowledge workers say they have the tools they need to be successful. What’s more, many explicitly request faster and more efficient hardware — a request that will likely increase as AI capabilities expand and improve.
Use data to guide device refresh decisions
CIOs have a clear opportunity to better align their device investments with AI productivity and user experience, ensuring a better ROI over time. One effective approach is to surface the data and insights needed to make targeted refresh decisions.
HP and AMD offer complementary capabilities that put data at the heart of device refresh decisioning. HP’s devices and digital employee experience solution provide visibility into device performance and stability, application experience, battery health, and employee sentiment. This visibility can be used to determine which devices genuinely support productivity and which create friction.
HP used its own Workforce Experience Platform (WXP) to guide its internal device refresh program. The results were compelling, with overall fleet performance improved by 9%. What’s more, 85% of employees who responded noted that the new PCs improved their productivity.
AMD has experienced similar benefits from its own device refresh strategy. In a recent blog, the company noted, “Instead of relying solely on device age, we used real-time endpoint data to identify systems that are most at risk — prioritizing refreshes for users experiencing instability or performance degradation. This targeted approach not only improved user satisfaction and productivity but also validated our belief that intelligent lifecycle management is a strategic IT advantage.”
Both examples demonstrate that organizations can prioritize investment based on actual performance and employee need rather than device age alone.
HP’s next-generation AI PCs and workstations leverage AMD Ryzen™ PRO processors to deliver consistent performance and efficiency across a wide range of enterprise AI-powered workloads. HP’s AI PC portfolio features dedicated neural processing units (NPUs) designed to accelerate complex AI tasks and create a more responsive experience. HP AI PCs, advanced by AMD, can perform up to 85 tera operations per second (TOPS), which ensures smoother and more efficient AI performance, especially when multiple AI features run simultaneously.
AI PCs purpose-built for AI processing offer a path to budget-friendly AI, handling smaller models and workloads locally with more predictability than consumption-based pricing. Even if organizations don’t currently run AI workloads at the edge, they should account for the possibility that more workloads will move to the edge during the typical device lifecycle. In addition to the cost benefits, running AI locally also reduces security risks by ensuring sensitive data remains on devices.
“Even if you’re not running AI locally today, chances are you will be over the course of the typical four-year lifecycle,” says Shaun Copper, Field Application Engineer at AMD. “You have to think about end user compute as part of the overall AI strategy and device lifecycle roadmap.”
The bottom line
In the AI era, a standardized, age-based refresh cycle is no longer enough. By combining device-performance data, employee-experience insights, and a clear understanding of emerging AI workloads, CIOs can direct investment where it will have the greatest impact while avoiding unnecessary refresh costs.
Learn more about how HP and its partner AMD can help set the stage for success.