Preparing data for analysis
Enrich sparse people records before running gap, succession or structure analysis on them.
— Organisation & Workforce
Turns raw people records into an analysed, structured view — skills, experience, seniority and context made consistent across the population so the data is usable for planning decisions.
2–3 days → ~10 minutes
For one complete, review-ready pass
No coding required
— USE CASES
Enrich sparse people records before running gap, succession or structure analysis on them.
Join CVs, exports and system data into one structured record per person.
Add the detail that makes an assessment or planning run meaningful.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
The run works from org data and role documentation you supply, so conclusions about the organisation in scope are anchored to your evidence rather than general model knowledge.
Content is restructured into the form your deliverable or downstream system expects, with each role kept intact.
People records are usually thin: a title, a team, maybe a start date. In practice it shows up as preparing data for analysis: enrich sparse people records before running gap, succession or structure analysis on them. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 2–3 days of capacity for it.
As a Skill, the work is already sequenced. You bring the evidence, and the run produces enriched, structured people records plus joined sources reshaped into one view. 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: 2–3 days down to ~10 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
People data enrichment 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.
Redesigns workflows and roles around the practical use of AI, producing a board-ready plan with task-level recommendations, costed capacity gains and clear workforce implications.
Keeps a standing read on the risk universe in scope, working through risk registers, controls and policy documents on a regular cycle to show where each risk stands, what has moved since the last run and which items now need an owner's attention.
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.
Runs a recurring skills survey across the workforce on a set cycle, building a living view of skills and utilisation by department that leadership can track month over month.
Ranks candidate locations against weighted workforce and business criteria, tests how cost assumptions affect the result, and produces a board-ready recommendation with a traceable evidence base.
Reviews a workforce metrics pack and states what the measures actually imply, rather than restating the numbers.