— Organisation & Workforce

Talent Equity

Assesses pay equity and workforce representation using anonymised, grouped data and sourced market benchmarks, identifying priority gaps, evidence-supported causes and practical actions.

  • Organisation & Workforce
  • Traceable reasoning
  • Runs anywhere
Preview methodology

1–2 weeks → ~15 minutes

Including the external research pass

No coding required

Input
Anonymised, grouped pay figures
Output
Pay equity and workforce representation diagnostic report
Runs in
Skillsize · ChatGPT · Claude · Copilot
Export
SKILL.md · MCP
Time saved
~1–2 weeks per run

— USE CASES

What people use Talent Equity for

Reviewing internal pay equity

Identifies pay differences across workforce groups and places them alongside market pay levels. Distinguishes observed gaps from possible explanations supported by the available evidence.

Assessing workforce representation

Compares workforce composition with relevant sector and country benchmarks. Highlights where representation differs from the market and which gaps merit closer attention.

Setting workforce equity priorities

Provides a prioritised view of pay and representation gaps, with practical actions and sourced benchmarks. Gives leadership a clear basis for deciding where to focus further investigation and improvement.

Supporting client equity reviews

Produces a diagnostic report that connects internal workforce evidence with external market context. Keeps conclusions grounded in grouped figures rather than assessments of individual employees.

What it works from

  • Anonymised, grouped pay figures
  • Anonymised, grouped workforce representation figures
  • Diagnostic scope and workforce group definitions
  • Sector and country context

What you get back

  • Pay equity and workforce representation diagnostic report
  • Internal pay gap and representation analysis
  • Comparison with current sector and country benchmarks
  • Prioritised gaps requiring attention / review / action

— HOW IT BEHAVES

How Talent Equity produces its result

The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.

Live research on the organisation in scope

Current external sources on the organisation in scope are researched during the run rather than recalled from training data, and every source travels with the output.

Composed as work product

Findings on the organisation in scope are written up as a document that reads like professional output, with each claim tied back to a role.

Delivered as a working table

The roles land as a clean table you can sort, filter or drop straight into the deliverable.

Why this is expensive by hand

Pay and representation figures can reveal differences without explaining whether they reflect internal inequity, wider market patterns or gaps in the evidence. Reviewing internal pay equity is the typical trigger — identifies pay differences across workforce groups and places them alongside market pay levels. Distinguishes observed gaps from possible explanations supported by the available evidence. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1–2 weeks of experienced attention.

How this Skill produces it

Skillsize turns that work into a Skill: you supply the material, and what comes back is pay equity and workforce representation diagnostic report, with internal pay gap and representation analysis. 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. The practical effect: 1–2 weeks of manual work becomes a ~15 minutes run, held to an identical standard on the tenth engagement as on the first.

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

Talent Equity 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)