Ask how much of their accrued time off each team actually takes and get a written read with an inline chart, with the low-utilization teams flagged as burnout risk. A snapshot on demand, grounded in your HRIS data.
Using our [HRIS] data, show me PTO utilization by [team] over the last [12 months] and flag any teams at burnout risk from low time-off.
An illustrative example. Your output will reflect your own sources, data, and prompts.
Reading your HRIS balances and leave usage for the trailing twelve months, the company is using 62% of accrued PTO. The spread by team is wide, and two teams sit low enough to flag as burnout risk, both called out below.
Across the company, people are using 62% of the paid time off they accrue over the last twelve months, which leaves roughly 3,100 accrued days sitting unused on the books. That average hides a wide spread between teams, and the low end is where the risk sits.
Here is utilization by team, from highest to lowest. Anything in red is well below the company average and worth a closer look.
PTO utilization by team: share of accrued time off actually used, last 12 months
Sales leads at 88%, with Marketing at 74% and Product at 69% — all comfortably above the 62% average, so those teams are genuinely disconnecting. Operations sits at 61%, right on the line. The concern is the bottom two: Engineering at 48% and Support at 41% are the only teams meaningfully below average, and chronically low time-off is a recognized burnout signal, not a sign of dedication.
Support is the one I would act on first. At 41% it is the lowest team on the board, and it is the same team the turnover read already flagged as your worst attrition hotspot. Low PTO usage and high exits tend to compound: people who never step away burn out and leave, which piles more coverage onto whoever stays, which pushes their usage down further. Engineering at 48% is the second flag — less acute than Support, but the same shape.
Sales at 88% is healthy, not alarming — high usage on its own is not an over-work signal, it just means that team is taking what it earns. The takeaway is narrow: two teams, Support and Engineering, are leaving most of their time off unused in a way that lines up with real attrition risk. A manager nudge to actually book time helps, but it is worth checking first whether workload or thin coverage is what is blocking them from taking it.
Want me to break Support's utilization down by manager to see where the low usage clusters, or cross the low-utilization teams against your turnover read to see where the two risks overlap?
The PTO Utilization Analysis recipe reads your HRIS balances and leave usage on demand and returns a written analysis with one inline chart: how much of their accrued time off each team actually takes, with the low-utilization teams flagged against your company average. It's produced when you ask, not a board that runs on its own.
Give Joy read-only access to your HRIS, or add an export with PTO balances, leave transactions, accrual entitlements, teams, and hire dates to the Knowledge Center.
Ask for PTO utilization over a window, broken down by team. Joy reads the data at ask time and computes each team's share of accrued time off used.
Joy returns a short written read with one inline chart ranking teams by utilization, low-usage teams flagged as burnout risk. The figures in the prose match the chart.
Copy the read and numbers into your manager one-on-ones or people review. Joy drafts the analysis; you raise it. Nothing is exported or scheduled for you.
Save this ask as a custom command on the assistant your team already uses, so anyone can run it in one step.
Calls out which teams are running well below your company average on time-off used.
A single inline chart ranks utilization by team, so the low-usage teams are unmistakable.
Sums the accrued time off sitting unused, so the exposure is a number you can quote.
Follow up by manager and Joy re-reads to see whether low usage is concentrated under one lead.
Break a flagged team's utilization down by people-manager to find where low usage clusters.
Compare utilization across offices or regions to catch site-specific coverage problems.
Cross low-utilization teams against your turnover read to see where the two risks overlap.
Focus on unused days at risk of expiring or being paid out so finance can plan for it.
No. It's a written analysis produced on demand with one inline chart — the name deliberately drops "Dashboard" to make that clear. When you ask, Joy reads your HRIS data and returns the read for that moment. There's no persistent board refreshing on its own.
Joy computes utilization as PTO taken over PTO accrued for the window you choose, then breaks it down by team. In the example it uses a trailing twelve months and a 62% company average as the baseline for flagging low-utilization teams.
Banked days look like savings on a balance report, but chronically low usage means people aren't recovering. In the example, Support at 41% and Engineering at 48% sit well below the 62% average, and Support is also the team the turnover read flags as the worst attrition hotspot — low usage and high exits tend to compound.
Yes. Ask a follow-up and Joy re-reads the data to slice utilization by manager or location, useful for tracing a low-usage team like Support back to whether it's concentrated under one lead or a workload problem across the board.
No. Joy drafts the analysis in chat; you copy it into your people review or manager one-on-ones and share it yourself. Joy doesn't schedule recurring reports or send anything on your behalf.
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