Performance and compensation calibration support
Calibration sessions determine ratings, raises, and promotions — but they're often inconsistent across managers. AI can surface patterns, flag outliers, and structure the discussion without replacing human judgment on people decisions.
What this workflow is
The process of reviewing and calibrating employee performance ratings and compensation decisions across managers, teams, and departments to ensure consistency, fairness, and alignment with organizational guidelines.
Why teams struggle with it
Managers rate differently. Some inflate, some compress. Calibration meetings become political negotiations rather than evidence-based discussions. Budget constraints force trade-offs that aren't always transparent. The process feels subjective even when the intent is fairness.
Why generic AI often fails here
Generic AI can summarize performance data but can't contextualize it within your compensation philosophy, band structures, or team dynamics. It doesn't understand that a '3' from one manager might equal a '4' from another.
Where AI can actually help
Distribution analysis across managers and teams. Outlier detection (unusually high or low ratings relative to peers). Compensation benchmarking against bands and market data. Structured calibration session preparation with data-backed discussion points.
Inputs the system needs
- Performance ratings by employee, manager, and department
- Compensation data (current salary, band, tenure, last increase)
- Historical rating distributions by manager
- Compensation philosophy and band structures
- Budget pool allocations
Outputs the system produces
- Rating distribution analysis by manager and department
- Outlier flags with context and evidence
- Calibration discussion guide with key talking points
- Compensation recommendation within band and budget constraints
- Post-calibration summary with changes and justifications
Controls that matter
- Final rating and compensation decisions belong to managers and HR leadership
- Outlier flags are recommendations, not overrides
- All calibration inputs and outputs must be confidential and access-controlled
- Budget impact must be visible in real time during calibration
- Audit trail required for regulatory and internal review
When this is not a good fit
When the organization has no performance review process, when compensation decisions are made solely by founders with no structured bands, or when employee count is too small for meaningful distribution analysis.
Calibration AI readiness rubric
- HIGH FIT: 200+ employees, formal review process, defined compensation bands, annual calibration cycle
- MEDIUM FIT: 50-200 employees, semi-structured reviews, emerging compensation framework
- LOW FIT: <50 employees, informal feedback culture, no compensation bands
- BLOCKER: No performance data history or no manager willingness to use structured tools