Playbooks·HR / People·Professional Services·HR16
Compensation Review Cycle Analysis
Extract compensation data, compare against benchmarks and equity signals, and produce budget-validated adjustment recommendations.
GenAI impact
What this playbook delivers
Projected against the same workflow run manually, end to end
- 60%
- Faster than the manual baseline
- ~9.2hrs
- Saved per cycle
- 3
- Source-of-truth artifacts
Estimated impact based on 15 employees in a single compensation review cycle · Real savings vary with team volume and current process maturity
The challenge
Why the workflow breaks
Three patterns show up across teams using GenAI without a shared workflow
Input reconciliation
Compensation teams have to reconcile performance packs, pay records, benchmarks, equity guidelines, role levels, tenure, budget parameters, and prior-cycle context before recommendations are traceable.
Equity blindspots
Pay gaps or inconsistent adjustments may be missed when benchmark comparison and equity checks happen separately.
Rationale gaps
Adjustment recommendations create risk when the reasoning cannot be traced to data, budget, and review criteria.
Where GenAI helps
From confused intake to source-of-truth artifacts
Here’s what changes when the team uses this playbook
From
To
Compensation data is cleaned ad hoc
Compensation Data Extract establishes the review set
Benchmarks are checked in separate files
Benchmark Comparison Matrix structures market context
Equity issues surface late
Equity Gap Analysis flags patterns before recommendations
Recommendations lack a defensible explanation
Compensation Recommendation Rationale documents the basis
How the playbook works
3 phases, one source of truth
Each phase produces an artifact the next phase builds on
Extract data
AI structures the Compensation Data Extract so review inputs are consistent before benchmark or equity analysis.
Compare benchmarks
The workflow creates a Benchmark Comparison Matrix and Equity Gap Analysis for human review of market and internal signals.
Validate recommendations
Draft Adjustment Recommendations become Budget-Validated Recommendations with a Compensation Recommendation Rationale.
What you’ll produce
Sample artifacts from the workflow
Interim and final deliverables you can review and download
Benchmark Comparison Matrix
Employee-level benchmark comparison with compa-ratios, position flags, and summary counts before equity analysis.
Equity Gap Analysis
Outlier and peer-equity gap analysis tied to internal thresholds, severity, and compensation risk notes.
Compensation Recommendation Rationale
Anonymized recommendation rationale tying proposed adjustments to benchmark position, internal equity flags, budget constraints, and approval logic.
Built into the workflow
Quality and risk checks at every step
Verification, data handling, and definition-of-done rules are part of the playbook — not afterthoughts
Quality
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.
Risk handling
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.
LIVE ONLINE LABS
Join a live Lab for this workflow
An AGASI expert runs your team through the Compensation Review Cycle Analysis workflow on your own real work — so you leave with the method, not a recording.
Built around this exact playbook
You internalise the method, not a recording.
Facilitated by an AGASI expert
Live online coaching on prompts, verification, and where the workflow tends to break.
Public or private formats
Join an open public Lab, or bring it in-house for your team.
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Run the Compensation Review Cycle Analysis playbook
Step-by-step prompts, role guidance, data-handling notes, and definition-of-done checks for every step of the workflow