There is a game that children in the 1980s played with plastic hippos, colored marbles, and a frantic urgency that the mechanics of the game produced automatically. The objective was to collect marbles faster than the other players. The hippos consumed whatever was in front of them without strategy or restraint, just constant mechanical appetite.
Old devices in a business environment work something like those hippos. They consume IT support time, employee patience, and operational budget with a consistency that people in the environment eventually stop noticing, because the consumption has been normalized. The slow startup has always been slow. The occasional freeze has always happened. The extra thirty seconds here, the spinning wheel there, the application that takes noticeably longer to open than it used to — all of it has always been part of the morning. The marble drain is invisible because everyone adapted to it before anyone thought to measure it.
The Forrester research on Mac device performance measured it. The findings confirmed what anyone working closely with aging device fleets already observes: devices beyond a certain age threshold cost significantly more to support and produce significantly more visible to users friction than newer hardware.
What the Forrester Data Found on Device Age
The Forrester research quantified the IT support cost difference between devices under three years old and devices over three years old in comparable business environments. The gap is not marginal. Older devices generate more IT support tickets, require more technician time per incident, and experience more hardware related failures that require escalated intervention.
The economic model that Forrester builds around this finding shows that the apparent savings from extending device refresh cycles beyond three years are offset by increased support costs well within the extended period. In most scenarios, a device held past three years costs more in cumulative support over its extended life than it would have cost to replace it at the three year mark.
This calculation is counterintuitive to most IT procurement processes, which evaluate hardware refresh as a line item cost and compare it directly to zero spend in years when no refresh is planned. The hidden variable is the support cost trajectory, which is not zero in the years the refresh does not happen. It is increasing, often substantially, as the device fleet ages.
The Specific Numbers That Drive the Math
The Forrester research on Mac in enterprise environments found that well managed Mac fleets generate 60 percent fewer support tickets than comparable PC environments, and each Mac support ticket costs 20 percent less to resolve than a PC ticket — delivering an $82 reduction in annual device support costs per Mac. That advantage is real, but it assumes modern hardware. As devices age past three years, accumulated software overhead, storage fragmentation, and hardware wear begin to close that gap. The support cost advantage that justified the platform choice starts to erode as the fleet ages.
The Forrester study measured the total five-year cost of ownership at $3,273 per MacBook Air versus $3,820 per PC — a $547 advantage per device. Across a fleet of 50 modern devices, that is $27,350 in five-year cost savings before productivity gains layer on top. Let those devices age past three years without refreshing them and you begin paying back that advantage in increased incident volume, longer resolution times, and more frequent escalations. The refresh decision is not just a capital question. It is a question of whether you retain the cost advantage you built the platform on.
The hardware cost of replacing those 50 devices is typically in the range of $60,000 to $80,000 depending on the specific configuration. That investment generates a per year reduction in support costs that, over a three year cycle, accumulates to meaningful savings.
The User Experience Dimension
The Forrester data on support costs captures the IT organization’s perspective on aging hardware. The user experience dimension captures something different, and in many ways more costly.
A device that has accumulated three or more years of use is typically slower than it was when it was new. The causes are multiple: storage fragmentation, accumulated software overhead, battery degradation, hardware component wear. The result is a user who waits longer for everything. The startup process takes meaningfully longer than it once did. The application launch takes longer. The file operation takes longer. The video call has more stutters.
None of these delays are emergencies. They do not generate support tickets. They do not appear in any IT metric. They appear only in the accumulated time that the person using the device spends waiting every day, and in the accumulated frustration that eventually influences how that person thinks about the organization’s tools and infrastructure.
The Forrester research captured the talent dimension in retention data. Organizations running well managed, modern Mac environments see consistent improvement in employee retention compared to organizations without that platform alignment. Forrester documented this through structured interviews and classified it as an unquantified benefit — meaning the pattern was real and consistent across every organization studied, but the magnitude varies enough by workforce that assigning a single number would be misleading. A device that performs well is invisible. A device that does not perform well is something people notice, complain about quietly, and eventually factor into their decision to leave.
The Hidden Productivity Tax
The most difficult component of the aging hardware calculation to quantify is the productivity effect, which is also often the largest in absolute dollar terms.
A user working on a device that starts slowly loses a cumulative five to ten minutes per working day compared to the same user on a modern device. The application that used to open instantly now takes 3 to 5 seconds longer. The file transfer that was instant now takes 15 to 20 seconds longer. The video call that used to start instantly now requires 10 to 15 seconds of connection initialization.
None of these delays individually are catastrophic. Together they represent a constant low level drag on productivity that compounds across the day. For a $100,000 per year employee, a five percent reduction in effective working time due to hardware friction represents $5,000 in annual productivity cost. For a team of 20 people with aging devices, that accumulates to $100,000 in annual productivity loss.
The Forrester research estimated these effects by surveying users about their perception of productivity impact and cross referencing those estimates with manager assessments of output quality. The resulting estimates tend to be larger than most organizations expect when they start this analysis for the first time.
The Calculation That Changes the Conversation
The conversation about hardware refresh typically happens in the context of a capital budget. How much does replacing these devices cost. The number is visible and uncomfortable. The conversation then focuses on whether that cost can be deferred, reduced, or amortized differently.
The conversation rarely includes: what does NOT replacing these devices cost. That calculation requires assembling several numbers that are usually tracked separately, if they are tracked at all.
The support cost increase for aging devices can be estimated from IT support ticket data. The productivity cost of slower hardware can be estimated from a combination of the device performance data and the hourly rate of the employees using those devices. The talent cost, the effect on retention and engagement of a work environment that consistently provides inadequate tools, is harder to quantify precisely but directionally clear.
When all of these costs are assembled, the decision about hardware refresh looks different than when only the purchase cost is visible. The Forrester research provides a framework for making that assembled calculation explicitly. The specific numbers vary by organization, but the directional finding is consistent: holding devices past the point where their support cost and user friction costs exceed their replacement cost is financially negative, even before accounting for the talent and morale effects.
What This Means for Hardware Refresh Strategy
The practical implication for businesses running Apple hardware is that a proactive refresh strategy, one that replaces devices on a defined schedule rather than waiting until they fail, is financially better than a reactive one in most scenarios. This is a conclusion that conflicts with the intuition of most procurement processes, which view deferred capital expenditure as a cost savings.
A managed hardware refresh program, typically on a three to four year cycle for most business use cases, keeps the device fleet within the performance and support cost range where the economics are favorable. It also creates predictable budget line items rather than unpredictable capital requirements when devices fail in clusters.
For businesses leasing hardware rather than purchasing it, the economics are often even more clearly favorable. The monthly lease payment for new hardware is predictable. The support cost variance of aging hardware is not. Predictable costs are easier to manage than variable ones.
The Conversation with the Finance Team
The Forrester research is useful in conversations with finance teams precisely because it provides external, quantified data rather than internal advocacy. The case for hardware refresh can sound like IT asking for budget. The Forrester data reframes it as an ROI calculation with supporting evidence.
The conversation shifts from: we want new devices, to: here is what continuing to hold these devices costs in support, productivity, and talent terms, compared to the cost of a refresh program. That reframing tends to produce different outcomes in budget discussions, because it asks the finance team to evaluate a complete economic picture rather than a line item cost in isolation.
The most persuasive version of this conversation includes a specific calculation for the organization: which devices are approaching the three year mark, what is their current support cost trajectory, and what would a replacement investment cost compared to the projected support cost increase. That specificity makes the argument concrete and the math impossible to dismiss as theoretical.