According to your dashboard, one of your tools has 400 active seats. Before long, finance asks whether or not they should be renewed.
The count may indicate that it should be renewed. But since it doesn’t tell you who uses the tool, let alone how much work runs through it, you won’t be able to get a confident answer from that number.
Research shows that many companies don’t fully utilize the number of seats in a tool they’re paying for. Zylo’s 2026 SaaS Management Index puts average license waste at $19.8M a year per organization, out of $55.7M in total SaaS spend.
Even a tool that most people have tried can take up very little of the workday. In our 2026 Global Trends and Benchmarks report, the average team member is using 18 apps per day. To better understand which tools are helping teams get the work done and make the right decisions with it, you need software usage insights.
Software usage insights are role- and team-level data on how much work time goes into each tool, used to decide what to buy, renew, consolidate, or roll-out. Logins show who has access, while workforce analytics shows which tools contribute to how work gets done.
SaaS management platforms handle inventory, renewal dates, and unused software licenses well. That record is the foundation of software license optimization.
Their data stops at access, though. A login can show that someone opened a tool, but it can’t show the extent to which they rely on it. Login data doesn’t answer the question of whether they used it for five minutes a week or five hours.
License data also doesn’t reveal when SaaS sprawl has left several tools doing the same job. Work-time usage data shows which tools overlap, complementing license data.
| License and access data | Work-time usage data | |
|---|---|---|
Answers | Who has a seat and who logged in | How much work time is spent in the tool, by role |
Typical source | SaaS management platform, SSO, billing | Time and app usage data from work devices |
Good for | Reclaiming unused seats, tracking renewal dates | Judging fit, depth of adoption, overlap, and impact |
Blind spot | Light use and core use look the same | Contracts and spend |
Tool sprawl costs more than the licenses. Zylo’s 2026 SaaS Management Index finds companies average 10 apps each for project management and team collaboration, and nearly seven for generative AI.
The cost extends to focus time. In our 2026 Global Trends and Benchmarks report, we found that some specialized roles use up to 36 apps per day. Specialized or not, teams with very high tool counts spend more time in messaging and coordination and less in deep work.
A 2022 Harvard Business Review study found that people perform an average of nearly 1,200 app or site toggles a day, totaling just under four hours a week reorienting.
A tool consolidation case built on license fees alone leaves out the focus time people lose working across overlapping tools. Make sure to include both numbers when presenting to finance.
There are four kinds of technology investment decisions: buy, renew, consolidate, and roll out.
Each one requires a different usage signal to support it. You need to match the right signal to the right decision before money moves, so each call is based on real evidence.
| Decision | Question to answer | Usage signal to check | Watch-out |
|---|---|---|---|
Buy | Does a tool we already own cover this need? | Work time going into tools that do the same job, plus generic apps or spreadsheets people use as workarounds | One team’s request can look like company-wide demand. Check if other roles show the same workarounds before buying. |
Renew | Is this tool part of core work for the roles we bought it for? | Share of work time those roles spend in it over the 60-90 days before renewal, and whether the trend is rising, flat, or fading | Burst-use tools like quarterly close or incident response, look light in a short window. Extend the window to cover a full cycle. |
Consolidate | Which overlapping tools can we retire with the least disruption? | Apps that serve one function, grouped together, with the work time each one carries across roles | Switching and retraining time for the roles that rely on the tool being retired. |
Roll out | Did the rollout change how work gets done? | Adoption and share of work time by role at 30, 60, and 90 days, measured against a pre-launch baseline | High adoption with shallow use points to a training or workflow-fit issue, so judge the rollout on work time as well as logins. |
Before making a new purchase, review if a tool you already own covers the need. Usage data answers that by showing where the work is happening now.
Suppose a team asks for a dedicated reporting tool. If its work time shows a large share going into spreadsheets, then you have a confirmed need. If it shows heavy time in a current tool built for reporting, the next step is training or configuration.
A single department’s request may not speak for the company. Compare how other departments spend their time; when the same pattern appears across several, the technology investment has a stronger case, and when it stays with the requesting team, a narrower fix might make more sense.
Check usage for the roles the tool was bought for. A design platform licensed company-wide may be used all day by designers and rarely opened by anyone else, with the seat count treating the two groups alike. Review the 60 to 90 days before the renewal date; this is long enough to show a pattern and still early enough to act on it.
Having an understanding of role-level usage lets you go into SaaS renewals knowing which seats to keep, right-size, or move to a lower tier. This is ultimately what software license optimization depends on.
Some tools are needed in bursts, while other tools can sit untouched for months (e.g., an annual audit platform) and still be essential, so check when it’s needed before cutting it.
Sorting your apps by what they do makes SaaS sprawl easier to see.
As an example, one group might hold three whiteboarding tools, another a few project trackers. Usage data shows how work time splits within each group.. The tool that takes the largest share of work time across roles is the natural one to keep, since retiring it would affect the most people.
Application rationalization goes more smoothly when the choice follows what people use. Software rationalization still asks something of the teams on the retired tools, which is time to move their work over and some training on the one that stays. Plan for that time before switching anything off.
Before launch, record how the target roles spend their time today so the rollout has something to be measured against.
Then, check back at 30, 60, and 90 days on two measures: adoption rate, or how many people in the role use the tool, and depth, or how much of their day it takes.
Software adoption can look healthy on the first number while the second stays small. When adoption is high and depth is low, look at how the rollout went before judging the tool. People may have been shown how to log in without learning how to work in it, or the tool may not fit how the work moves.
AI rollouts can follow this pattern, with strong adoption on day one but the work itself unchanged.
Software usage analytics relies on specific measurements tracked by role and over time. Six of them support the four decisions above, and they work best when you pull them for the same roles and window every time. This way, a change in a number points to a change in how people work.
Application usage analytics at this level starts with app activity recorded on work devices and grouped by team and role. Hubstaff Insights reports that work-time data by role.
Judge an AI tool investment by how much work it is automating and enabling. The count of people who have simply tried it says little about whether anything changed.
Zylo’s 2026 SaaS Management Index found that average spend on AI-native apps rose 108% in a year, to $1.2M per organization. AI also had the fastest-growing app count of any category in its dataset, up 181%. Spending that’s rising this quickly calls for a closer look at what it’s buying.
Start with breaking usage down by role. When a role has high adoption but only a small share of its work time in AI tools, the tools are usually handling quick drafts and summaries. It doesn’t meaningfully change how the work moves, which is a sign the role needs enablement more than another seat. On the other hand, a large share means AI has become part of the workflow itself, and more seats are easier to justify there.
AI adoption metrics are most useful when you collect them before the next purchase, so be sure to measure these:
Pulling the same data supports AI policy and approved-tools checks. It also shows which AI tools each team uses, so any shadow AI appears in the data next to the approved tools. For a side-by-side comparison of options, check out tools that help monitor AI usage.
To make sure each renewal accurately reflects the way your teams work, run a usage-based review ahead of every renewal cycle. Assign it to one person in IT or operations, so the steps stay consistent from one round to another. The six steps below take you from your current list of tools to decisions you can act on and measure afterward.
Repeat the steps each cycle. The records will eventually add up, and after a few rounds, you’ll see which kinds of purchases stay in steady use and what the decisions from earlier reviews saved, which gives the next review a benchmark.
For a technology review to reach sound conclusions, it has to work from team and role data combined across many people. After all, a decision to renew or retire a tool has no use for a ranking of individuals.
Tell your team members at the start what is measured and why. This allows the review to be read as an evaluation of tools, the purpose of which nobody has to guess.
That openness extends to the data itself:
These practices put transparency, access, and control—the guiding principles that shape how Hubstaff operates—to work. Hubstaff is SOC 2 Type II compliant and built to operate within GDPR and HIPAA requirements. You can learn more about its data retention controls in our Trust Center.
We’ve covered how to judge the need for a tool before you buy it and how well it’s used afterward. Both depend on usage data by role and team, which Hubstaff supports with app usage analytics drawn from how work time is spent across apps and websites.
Hubstaff complements your license and spend tools. Contracts and seats stay with them, and Hubstaff adds application usage monitoring data on how each tool is used across roles and teams.
Seat counts show who has access to a tool, while work-time data by role shows how much of each team’s day runs through it. Before your next technology investment, review how much real work the tool supports and which roles use it, so the money goes where the work is.
Hubstaff’s workforce intelligence turns work-time data into answers about how your teams spend their day. Book a demo to see how it can help you invest in the tools that meaningfully improve how your team works.