Resourcing a new strategy
Defines the skills a strategic ambition demands and assesses how well the existing team covers them. Recommends roles and headcounts linked to specific capability gaps.
— Talent & Leadership
Compares the skills a team actually holds against the skills its strategy demands, then recommends the specific roles — and how many of each — needed to close the gap, with the cost of hiring them.
1–2 weeks → ~15 minutes
For a typical multi-document review workflow
No coding required
— USE CASES
Defines the skills a strategic ambition demands and assesses how well the existing team covers them. Recommends roles and headcounts linked to specific capability gaps.
Provides a costed recruitment proposal grounded in capability needs rather than vacancy requests alone. Makes the assumptions behind indicative costs explicit for budget discussions.
Creates an evidence-based view of skills documented across team CVs and professional profiles. Highlights areas where limited evidence leaves capability uncertain, rather than treating every undocumented skill as absent.
Connects each recommended role to the strategic requirement it supports. Gives decision-makers a clear basis for assessing the need for additional headcount.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
A coverage pass shows which candidates are supported by evidence and which are not — including requirements with nothing behind them and material that supports nothing.
What the candidate pool requires and what it currently has are set against each other candidate by candidate, so each gap is quantified rather than described in general terms.
Skills and requirements are read out of CVs, applications and interview notes as structured data rather than inferred from job titles.
All of your CVs, applications and interview notes is processed as one set, so patterns across documents surface instead of being read one file at a time.
Findings on the candidate pool are written up as a document that reads like professional output, with each claim tied back to a candidate.
Recruitment plans often reflect immediate vacancies rather than the capabilities a strategy requires. In practice it shows up as resourcing a new strategy: defines the skills a strategic ambition demands and assesses how well the existing team covers them. Recommends roles and headcounts linked to specific capability gaps. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 1–2 weeks of capacity for it.
Here the same job runs as a Skill. Your material goes in; evidence-based assessment of current team skills comes out, alongside skills requirements linked to strategic objectives. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. Net effect: 1–2 weeks down to ~15 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Recruitment Needs Analysis 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.
Builds evidence-backed employee skills profiles within HR systems, mapping documented experience to the organisation’s skills catalogue and assigning proficiency levels using consistent rules.
Assesses how clearly people explain their technical work against a consistent four-level rubric, producing a human-approved report with quoted evidence behind every score.
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.