AiOS→HR / People→Reward & Govern→HR16
Compensation Review Cycle Analysis
Extract compensation data, compare against benchmarks and equity signals, and produce budget-validated adjustment recommendations.
Consistent compensation reviews with documented rationale reduce pay equity risk and give managers defensible adjustment decisions.
GenAI Impact
60%
Faster
9.2
Hours saved
15.3
Hours without AI
Based on: 15 employees in a single compensation review cycle
Structured compa-ratio templates and step-by-step evidence-chain prompts ensure every employee receives benchmark comparison against identical criteria, eliminating the inconsistent ad-hoc analysis that occurs when analysts manually review compensation data under time pressure.
Enforced anonymization-before-prompting steps and data warnings prevent individual salary figures and employee identifiers from being pasted into public GenAI tools, mitigating the PII leakage risk inherent in ungoverned Shadow AI compensation analysis.
Before You Start
This workflow processes individual compensation records, performance ratings, and pay band data. Do not paste personally identifiable salary figures or employee names into public or unapproved GenAI tools.
GenAI may misinterpret benchmark ranges or fabricate market data points. Verify every benchmark comparison against the original source data before using recommendations in review discussions.
Who's Involved
Compensation Analyst
Runs the review cycle analysis and drafts adjustment recommendations for each employee.
HR Manager
Validates equity findings and approves the final recommendation rationale.
Finance Partner
Confirms budget alignment and approves allocation parameters before recommendations are finalized.
Execution Steps
Before you start
Data Handling: Do not paste unredacted employee names or exact salary figures into the prompt; use anonymized identifiers and rounded ranges.
Inputs
Prompt
Extract compensation and performance data points
CONTEXT You will be provided with the following source documents: 1. Performance Review Pack 2. Compensation Benchmark Data 3. Current Compensation Records 4. Internal Equity Guidelines 5. Budget Allocation Parameters TASK Extract the following data points for each employee: current base compensation, variable compensation, total compensation, performance rating, tenure, role level, and any flagged development areas. Present the data in a structured table using anonymized employee identifiers. OUTPUT FORMAT Produce a markdown table with these columns: | Employee ID | Role Level | Tenure (years) | Current Base | Variable | Total Comp | Performance Rating | Key Development Flags | Use anonymized identifiers (e.g., EMP-001) rather than names. Round compensation figures to the nearest thousand. After the table, add a one-line count: "Total employees extracted: [N]" CONSTRAINTS Do not include personally identifiable information such as names or contact details. Do not infer compensation data not present in the source documents. Only include employees listed in the provided records.
Outputs
Verification: Verify the AI has not omitted employees or misread compensation figures by spot-checking at least three entries against the source records.
Before you start
Inputs
Prompt
Compare compensation against market benchmarks
CONTEXT You will be provided with a Compensation Data Extract (structured table of employee compensation and performance data) and Compensation Benchmark Data (market survey ranges by role level and geography). TASK For each employee in the extract, compare their total compensation against the relevant benchmark range. Calculate the compa-ratio (actual total compensation divided by benchmark midpoint) and classify whether each employee falls below range, within range, or above range. OUTPUT FORMAT Produce a markdown table with these columns: | Employee ID | Role Level | Total Comp | Benchmark P25 | Benchmark P50 | Benchmark P75 | Compa-Ratio | Position vs Range | Position vs Range values: [Below Range | Lower Quartile | Mid-Range | Upper Quartile | Above Range]. Follow the table with: ## Summary - Count of employees in each position category - Average compa-ratio across the group - Range of compa-ratios (lowest to highest) CONSTRAINTS Do not fabricate benchmark figures — use only the ranges provided in the benchmark data. Do not adjust or normalize compensation figures beyond what is stated in the source data. Only use benchmark categories that match the role levels in the extract.
Outputs
Verification: Verify that AI-calculated compa-ratios match your manual spot-check of at least three employees against the benchmark source data.
Before you start
Inputs
Prompt
Outputs
Before you start
Inputs
Prompt
Outputs
Verification: Verify that AI-generated adjustment amounts do not introduce values absent from the benchmark or equity analysis inputs.
Before you start
Inputs
Prompt
Outputs
Before you start
Data Handling: Do not include the full budget allocation spreadsheet in the prompt; reference only the approved scenario totals and funded adjustment list.
Inputs
Prompt
Outputs
Verification: Verify the executive summary figures match the detail section totals before sharing with senior leadership.
Reference
Guardrails
- Evidence-Based Adjustments Only — Every recommendation must trace to a specific benchmark comparison or equity gap finding, not subjective judgment or unsubstantiated AI-generated rationale.
- Anonymize Before Prompting — Remove personally identifiable information from compensation data before including it in any GenAI prompt; use anonymized identifiers and rounded figures.
- Budget Envelope Binding — Recommendations must fit within the stated budget allocation; do not accept AI-generated adjustments that exceed available funds without explicit human approval.
Pitfalls
- Pasting individual employee names and exact salary figures into the GenAI prompt without anonymizing first.
- Accepting AI-generated benchmark comparisons without verifying the source data ranges match your organisation’s market and geography.
- Using AI-recommended adjustment percentages directly without validating against your internal equity guidelines and pay band structures.
- Skipping the budget validation step and presenting recommendations that exceed the approved allocation envelope.
Definition of Done
- Every recommended adjustment links to a specific benchmark comparison or equity gap finding in the rationale document.
- The Compensation Recommendation Rationale includes a traceable evidence chain that a non-specialist reviewer can follow.
- Total recommended adjustments fit within the stated budget allocation parameters.
- No personally identifiable information appears in any AI-generated artifact.
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AGASI AiOS · HR16 v1.0 · Apr 8, 2026