Bench time seen before it happens
Upcoming availability is matched against real demand, so idle weeks are visible early enough to redeploy or sell into.
— Organisation & Workforce
Bench & Pipeline Match
Matches available consultant capacity against the work coming down the pipeline each week, showing where people are about to sit idle, where demand will outstrip the skills you have and which pairings genuinely fit.
- Organisation & Workforce
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Current and upcoming availability across the team, with skills and seniority
- Output
- A ranked set of matches between available people and pipeline demand
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Bench & Pipeline Match for
Coverage gaps named
Where the pipeline needs skills the bench does not hold, the gap is stated plainly, with enough notice to hire, train or partner.
Better fit, not just any fit
Each possible pairing is judged on how well the skills actually meet the requirement, so the strongest matches lead rather than the first available person.
A trend on utilisation
Bench days are tracked week on week, giving a real utilisation trend instead of an argument about the baseline.
What it works from
- Current and upcoming availability across the team, with skills and seniority
- The pipeline of work with its skill and timing requirements
- Who owns resourcing decisions for follow-up
What you get back
- A ranked set of matches between available people and pipeline demand
- A named list of coverage gaps and where demand exceeds capacity
- A week-on-week bench trend and a short read on coverage for leadership
— HOW IT BEHAVES
How Bench & Pipeline Match produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Every row of the pipeline in scope
Each row of your export is processed on the same basis, so no account is skipped however long the table is.
The pattern charted
Movement across the pipeline in scope is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the pipeline in scope are written up as a document that reads like professional output, with each claim tied back to a account.
Each account enriched in place
Derived columns are added row by row, keeping your source data and the judgement about each account side by side.
Roll-up alongside account-level detail
Detail rows are summarised into the grouped view of the pipeline in scope without losing the underlying accounts.
Why this is expensive by hand
Resourcing decisions are usually made from two separate views: who is free, and what is coming. Reconciling them is manual, so bench time is spotted late and skill gaps only become visible when a project is about to start. In practice it shows up as bench time seen before it happens: upcoming availability is matched against real demand, so idle weeks are visible early enough to redeploy or sell into. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 2–3 weeks of senior time whenever it comes round.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is a ranked set of matches between available people and pipeline demand, with a named list of coverage gaps and where demand exceeds capacity. 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. Net effect: 2–3 weeks down to ~40 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Org design and workforce planning leads
- COOs and functional leaders reshaping teams
- Transformation consultants sizing people impact
- HR business partners supporting redesign
Run it in Skillsize — or export it anywhere
Bench & Pipeline Match 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.
ChatGPTClaudeCopilotAI Products (MCP)
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