Teams under sustained pressure
Highlights teams whose absence, engagement or overtime patterns sit outside workforce norms. Provides a watchlist with a proposed intervention for each flagged team.
— Talent & Leadership
Identifies unusual absence, engagement and overtime patterns, projects absence rates six months ahead and produces a team watchlist with targeted interventions to support earlier action.
1–2 weeks → ~15 minutes
For a full population, not a sample
No coding required
— USE CASES
Highlights teams whose absence, engagement or overtime patterns sit outside workforce norms. Provides a watchlist with a proposed intervention for each flagged team.
Projects where absence rates could land over six months if current trends continue. Gives leaders a conditional outlook to inform capacity planning and support priorities.
Compares flagged employees and teams with the rest of the workforce. Helps advisers distinguish concentrated pressure from organisation-wide patterns and frame more targeted support.
Provides an evidence-based summary of emerging absence and workload concerns. Grounds discussion of team interventions in observed patterns rather than anecdote alone.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Each row of your export is processed on the same basis, so no person is skipped however long the table is.
Findings on the talent population are written up as a document that reads like professional output, with each claim tied back to a person.
Detail rows are summarised into the grouped view of the talent population without losing the underlying people.
Absence and workload pressures can build unevenly across an organisation, leaving leaders unsure which teams need support and whether isolated concerns reflect a wider pattern. Teams under sustained pressure is the typical trigger — highlights teams whose absence, engagement or overtime patterns sit outside workforce norms. Provides a watchlist with a proposed intervention for each flagged team. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1–2 weeks of manual work.
As a Skill, the work is already sequenced. You bring the evidence, and the run produces summary of unusual absence, engagement and overtime patterns plus comparison of flagged groups with the wider workforce. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. In effect, 1–2 weeks of senior time compresses into ~15 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
Absence & burnout early warning exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
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Reveals how organisational culture is lived through anonymous employee accounts, producing an evidence-backed diagnostic of shared themes, differences between functions and gaps between values and practice.
Turns a high-potential's assessment evidence into an acceleration plan weighted to real experience and exposure, naming the stretch assignments, mentors and programmes that will compound fastest.
Builds a calibrated, reusable benchmark from source material so every later screen, shortlist or review is graded against the same bar.
Screens candidate evidence against a saved Blueprint, applying the same calibrated bar and traceable reasoning to every applicant.