DEI Aggregate Reporting Agent
A dei aggregate reporting agent is an AI agent for employee engagement & listening that assembles representation, hiring, promotion and pay-gap views at aggregate level with suppression below minimum group sizes, prepares inputs for statutory reporting such as EU pay transparency and gender pay gap filings, and drafts the narrative for leadership.
How does the dei aggregate reporting agent work?
What flows in, what the agent does with it, where a person decides, and what comes out.
Reads from
Demographic data held lawfully · Aggregate pay data · Hiring and promotion aggregates · Jurisdiction reporting rules · Minimum group-size rules
AI agent · runs when the reporting cycle or a statutory filing date nears
DEI Aggregate Reporting Agent
A person decides
Head of People and counsel review, sign off and file
Produces
Aggregate DEI dashboard · Draft statutory pack · Narrative with caveats · Suppressed-cell log
What does the dei aggregate reporting agent do?
Assembles representation, hiring, promotion and pay-gap views at aggregate level with suppression below minimum group sizes, prepares inputs for statutory reporting such as EU pay transparency and gender pay gap filings, and drafts the narrative for leadership.
What does it produce?
An aggregate DEI dashboard, a draft statutory report pack for review, and a plain-language narrative with caveats on data quality
Who decides?
The Head of People and legal counsel review, sign off and file; the agent compiles and drafts, it does not file, and it never surfaces individual-level protected characteristics.
What systems does the dei aggregate reporting agent connect to?
Examples of the kind of systems this agent would read from or write to, so you can picture it in your own stack. The actual set is whatever you run.
-
HRIS
demographics and lifecycle events
-
Compensation
pay data at aggregate level
-
Data warehouse / BI
suppression rules and dashboards
What data does it need?
- HRIS demographic data where lawfully collected
- compensation data at aggregate level
- hiring and promotion outcomes at aggregate level
- jurisdiction reporting rules
- minimum group size rules
How would you measure it?
reporting cycle time per statutory filing, annually; suppression and reconciliation checks passed, per report; representation and pay-gap figures, annually at the legally required grain
What does a first proof look like?
Rebuild last year's gender pay gap or pay-transparency report from the same source data in a restricted environment. Legal and the analyst compare figures line by line.
You'd call it working when
The numbers reconcile, every cell below the minimum group size is suppressed, and the narrative caveats match known data gaps.
What usually goes wrong?
- Demographic fields collected differently across countries treated as one
- Small-cell suppression that fails when two reports are combined
- Narrative that explains gaps away instead of stating them
What are the guardrails?
- Aggregate only, minimum group size enforced in every cell and cross-tab
- Never surfaces or infers an individual's protected characteristics
- Uses only demographic data lawfully collected for the purpose in each jurisdiction (GDPR special-category rules)
- Works council and DPO consulted before any new demographic analysis
- Never files; every draft and sign-off logged
What leaves your boundary is set per build; the inputs above are the ceiling, and where the model runs, what it retains, and the DPA are agreed with your security team before anything is connected.
Our read
Clearly valuable with real deployments behind it. Needs care on data and adoption.
Parts of this may exist in your current tools. The case for building is usually the join across systems, or your rules and language, that a suite feature cannot carry.
Protected characteristics; regulated reporting under EU pay transparency and equalities law; must be aggregate-only with strict suppression
Where it sits in the order
Highest sensitivity in the area; needs data governance and suppression rules proven on lower-stakes aggregates first
Is a DEI Aggregate Reporting Agent worth building for your function?
That depends on your numbers, your data, and what else is on the map for you. The strategy month works that out.
Thirty minutes. Bring the number this would move.