Voice of IT analysis: Insights into laptop tech debt

In this month’s Spiceworks Voice of IT survey, more than 400 IT pros shared their organization’s current practices related to laptops. This deeper dive was especially interesting, given that laptops were the number one subcategory for IT spending in the higher-level hardware category, according to the recently released Spiceworks Ziff Davis State of IT 2026 report —  representing an average of 20.9% of total hardware budgets.

(Another 14.9% and 8.2% of total hardware budgets went to desktops and tablets/mobile devices, respectively, underscoring the critical role of end-user devices in modern IT.)

Example: Tech Debt Drag for Laptops

“Tech Debt Drag” refers to the accumulated burden of older systems or practices that result in additional time or cost in the future. We can see evidence of Tech Debt Drag related to laptops in the Voice of IT findings. In a nutshell: The smaller the organization, the slower and less programmatic the refresh/replacement cycle for laptops.

Let’s look at the “less programmatic” side first. Based on the number of employees — small (1-99), medium (100-999), and large (1,000+) — Large organizations deal with 10.9% of laptop refreshes/replacements on an ad hoc/as-needed basis; 89.1% are done in a planful, programmatic way.

For medium the split was 26.1%/73.9%, and for small it was 31.2%/68.8%. See the chart below.

Undoubtedly, there’s a practical logic of being frugal (“if it ain’t broke, don’t refresh or replace it”), especially when IT budgets need to be stretched. Defer those investments until a later time. Roll out new laptops only when absolutely necessary, not based on an arbitrary schedule.

However, in times when IT may be understaffed, there’s an inherent hit on productivity (and job satisfaction) from higher-stakes, interrupt-driven, time-sensitive rollouts compared to that of a routine, planful approach. Using large organizations as a baseline, the incremental impact of being less programmatic is between 15% (for Medium) and 20% (for Small).

Now let’s turn to “slower.”

The following chart excludes ad hoc or on-demand laptop refresh/replacement cycles, which leaves us with those that are scheduled. The x-axis represents the number of years, and the y-axis indicates the cumulative percentage of laptops that have been refreshed or replaced after a specified period.

The smaller the organization, the slower the cycle — even when they’re planful. But in times of rapidly accelerating capabilities (e.g., the current AI era), there’s an inherent penalty for being slower.

Using large organizations as the baseline, the accumulated burden of older laptops on business outcomes (e.g., downside risks, cost savings, and upside opportunities) remains the same: between 15% for medium and 20% for small.

(And when you think about it, that’s just for one cycle! The faster you complete one cycle, the sooner you start the next cycle. So “Tech Debt Drag” for laptops compounds over time, like the interest on financial debt.)

What can be done?

Admittedly, for small and medium organizations, these insights call for a more thorough analysis of two scenarios.

Current state:

• Laptop refresh/replacement cycles that are slower and less programmatic
• Current total cost of laptop refresh/replacement cycles
• Negative impact on the productivity and job satisfaction of IT staff from a higher percentage of higher-stakes, interrupt-driven, time-sensitive rollouts
• Negative impact of older laptops on the three key categories of business outcomes: downside risk (cost avoidance), operational efficiencies (cost savings), and upside opportunities (revenues, profits, customer acquisition, competitive advantage)

Future state:

• Laptop refresh/replacement cycles that are faster and more programmatic
• Increased total cost of more frequent laptop refresh/replacement cycles
• Decreased costs due to the increased productivity and job satisfaction of IT staff
• Decreased costs (cost avoidance, cost savings) and increased revenue/profit (upside opportunities)

It could be as simple as this: for a given frequency, if the total cost per user to roll out a new laptop is $X, we can calculate the incremental cost of faster and more programmatic rollouts as some multiple of $X. For the sake of illustration, let’s call it $2X. So the question becomes: for the incremental $X per user of investment, how much incremental value would we get? As some folks say, “Is the juice worth the squeeze?”

Even if it’s mostly qualitative at first, wouldn’t it be prudent to think critically and strategically about the hidebound “if it ain’t broke, don’t fix it” assumptions that underlie the status quo?

I think yes.