For most of the last decade, endpoint budgeting was a low-drama exercise. Unit prices moved a little each year, refresh cycles were predictable, and IT could extend last year's numbers forward with reasonable confidence. AI-specific devices have broken that pattern.
Neural processing units (NPUs), higher baseline memory, and faster local storage, all the hardware needed to run on-device inference and AI-assisted productivity tools at acceptable speed, are adding real cost to every device category, and most three-year IT budgets haven't caught up.
The IT organisations getting caught out aren't the ones with bad forecasting models - they're just the ones still specifying and budgeting devices the way they did before AI features became a baseline expectation.
Read on for what a three-year hardware plan needs to account for today, and how a leasing solution can help.
A standard knowledge-worker laptop used to be defined by CPU generation and a modest memory/storage tier. That specification no longer holds.
Running local AI inference and increasingly AI-embedded productivity software at acceptable performance now effectively requires a dedicated NPU, a higher memory floor (16 to 32GB of RAM), and faster storage to keep local model and cache data from bottlenecking the rest of the machine. None of that is optional trim anymore; it's becoming the standard configuration, and IT teams pricing new fleets off last cycle's standard build are underbudgeting by a meaningful margin per unit.
The practical fix: reprice your standard build against current NPU/memory/storage requirements before locking in next year's unit cost assumption, rather than inflating last year's number by an assumed percentage.
AI-enabled devices offer a tangible boost to your end-users, but require significant investment compared to previous baselines.
AI workloads are pushing memory and local storage costs up disproportionately relative to the rest of the bill of materials. CPU packaging, chassis, and display costs haven't moved nearly as much as DRAM and NAND pricing tied to AI-driven demand. That means device refresh budgets built on a single blended inflation number will systematically underprice higher-memory configurations, which is exactly what IT teams now need to account for.
Budget planning needs to separate memory/storage costs from overall device costs, because the two are no longer moving in the same way they used to.
The biggest budget risk isn't the AI premium itself, rather it's applying it uniformly across a fleet where only a subset of users actually need full local AI capability. Developers, analysts, and power users running local inference or AI-assisted tooling heavily may justify the higher-memory, higher-NPU configuration. A large share of standard knowledge-worker seats may not need the top configuration at all, at least not yet, especially if AI features are delivered via cloud/hybrid processing rather than fully on-device.
Without a deliberate tiering decision, procurement defaults tend toward one of two expensive mistakes: buying the premium AI configuration fleet-wide "to be safe," or standardising on the old baseline and getting caught flat-footed as more software assumes NPU availability.
A three-year plan needs an explicit persona-based tiering model, reviewed annually as AI feature requirements shift.
AI-enabled productivity suites, copilots, and assistant features increasingly carry their own per-seat licensing on top of the hardware premium, and in some cases the licensing tier itself assumes a minimum device capability to function well. That means the AI cost shock isn't isolated to the capital hardware line. It also shows up in the operating software budget, and the two need to be planned together rather than as separate line items owned by different budget holders.
IT and finance teams that model hardware refresh and AI software licensing independently tend to under-forecast the combined cost of an AI rollout, because each side assumes the other is holding some of the cost.
The core problem with AI-driven hardware cost is really a timing problem: the definition of an adequately equipped device is moving faster than most capital cycles are built to handle. Leasing addresses that mismatch more directly than outright purchase does, for a few concrete reasons.
First, it converts a large, uncertain capital outlay into a predictable operating expense. Instead of committing capital today against a device specification that may look underpowered in eighteen months, IT pays a known amount per seat per period and keeps cash available for the parts of the AI rollout that are harder to predict, like software licensing and integration work.
Second, leasing shortens the practical distance between "the AI has moved the baseline" and "our fleet reflects the move" paradigm. A three- or four-year owned asset sitting on the books is a sunk cost that discourages early replacement even when it no longer meets AI performance expectations. A lease term that's deliberately matched to the AI hardware cycle rather than the traditional refresh calendar gives IT a built-in, already-budgeted exit point to move to current-generation NPU and memory configurations without a fresh capital approval discussion each time.
Third, it transfers residual value risk to the lessor at exactly the moment that risk is hardest to price internally. Nobody has great visibility into what a 2026-spec AI PC will be worth on the secondary market in three years, because the AI hardware curve is still moving. Leasing companies are already pricing that uncertainty into their terms; owning the asset means the enterprise carries a depreciation and obsolescence risk it has little ability to forecast accurately right now.
Finally, leasing supports the persona-based tiering approach directly. It's operationally and financially easier to lease a smaller number of high-memory, high-NPU devices for AI-heavy roles on a shorter refresh term, while leasing standard-tier devices for the rest of the fleet on a longer term, matching commitment length to how quickly each tier's requirements are likely to shift. Doing the equivalent with owned assets means carrying mismatched depreciation schedules across the fleet, which is harder to track and harder to explain in a budget review.
The IT organisations navigating this well aren't the ones with the most optimistic device forecast. They're the ones who've made AI hardware requirements an explicit, separately-tracked line in the plan. That means:
AI-driven hardware requirements have moved from an emerging consideration to a core input on IT capital planning, and the next three years of device spend will reward IT and finance teams that treat it as its own tracked variable.
A plan that explicitly tiers AI requirements by role, reprices the standard build accordingly, and uses leasing to keep pace with a fast-moving baseline will absorb this shift effectively and ensure their IT end-users are equipped with the tools they need today.