Ad-hoc Analysis Drafting Agent

An ad-hoc analysis drafting agent is an AI agent for people analytics & reporting that takes a leader's question (why did time-to-fill jump in Sales this half? is early attrition worse for remote hires?), drafts an analysis plan, runs it against the governed data, and writes a first-draft findings memo with methods, caveats and what the data cannot say.

How does the ad-hoc analysis drafting agent work?

What flows in, what the agent does with it, where a person decides, and what comes out.

  1. Reads from

    Leader's question · Approved people data and definitions · Metric dictionary · Prior analyses · Senior-review rules for pay and protected data

  2. AI agent · runs when a leader asks an analysis question

    Ad-hoc Analysis Drafting Agent

  3. A person decides

    People analyst reviews method and decides what is released

  4. Produces

    Analysis plan and code · Draft findings memo · Caveats and follow-up questions

What does the ad-hoc analysis drafting agent do?

Takes a leader's question (why did time-to-fill jump in Sales this half? is early attrition worse for remote hires?), drafts an analysis plan, runs it against the governed data, and writes a first-draft findings memo with methods, caveats and what the data cannot say.

What does it produce?

A draft memo with charts, the analysis plan and code, a caveats section and a list of follow-up questions.

Who decides?

A people analyst reviews method and interpretation and decides what is released; leaders decide what to do with it. The agent drafts and self-checks; it does not publish. Questions involving protected characteristics or pay are routed for senior review before any output leaves the team.

What systems does the ad-hoc analysis drafting 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.

  • Data warehouse

    governed data to analyze

    SnowflakeBigQueryDatabricks
  • BI

    charts and existing logic

    Power BITableauLooker
  • Documents

    memos and prior analyzes

    Microsoft 365Google WorkspaceConfluence
  • HRIS

    source attributes

    WorkdaySAP SuccessFactors

What data does it need?

  • governed people data model
  • metric dictionary
  • prior analyzes for reuse
  • role-based access rules

How would you measure it?

analyst turnaround per request, monthly; share of requests answered within a week; drafts needing method corrections, monthly

What does a first proof look like?

Take five real requests answered last quarter. The agent drafts plan, analysis and memo for each without seeing the original; the analyst who did the work grades method, numbers and caveats. Then run live for a month with every draft reviewed.

You'd call it working when

Drafts are right and review is faster than starting from scratch.

What usually goes wrong?

  • Plausible memo, wrong denominator
  • A pay or protected-group question slips through without senior review
  • Caveats section is boilerplate rather than specific to the question

What are the guardrails?

  • Nothing leaves the analytics team without analyst review
  • Pay and protected-characteristic questions routed to senior review before output
  • Aggregate outputs only, above the minimum group size
  • Every plan, query and draft logged and reproducible
  • Uses dictionary definitions; any deviation stated in the memo

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

Strong case sensitivity medium Order: after a first win

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.

Aggregate analysis that can touch pay or protected characteristics; interpretation errors carry legal weight.

Where it sits in the order

Needs the governed model and dictionary; earns trust after the query agent shows answers can be checked.

Is an Ad-hoc Analysis Drafting 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.

How the strategy month works

Book a call

Thirty minutes. Bring the number this would move.