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Calibration & Compensation Alignment

AI-assisted performance calibration across managers with anomaly detection, pay equity analysis, and pay-for-performance modeling achieving 70-85% time savings and ensuring fair compensation decisions.

Business Outcome

time reduction in the calibration process

Complexity:

Medium

Time to Value:

3-6 months

Why This Matters

What It Is

Current State

(Traditional)

1. Managers submit performance ratings for their teams to HR (no cross-manager discussion). 2. HR schedules 8-12 hour calibration session with all department managers in conference room. 3. Managers debate ratings case-by-case: 'Why is Sarah rated 4.5 while Bob is 4.0 with similar accomplishments?'. 4. Forced distribution applied: 10% top performers, 70% meets expectations, 20% needs improvement (creates unhealthy competition). 5. Compensation decisions made after calibration in separate process (ratings → merit budget allocation in spreadsheet). 6. No systematic pay equity analysis (gender, race, tenure pay gaps undiscovered).

Characteristics

• Peoplebox.ai
• Lattice
• Betterworks
• SAP SuccessFactors
• Workday
• Oracle HCM
• Excel

Pain Points

⚠ Time and resource intensity of the calibration process.
⚠ Process fragmentation leading to inconsistencies across teams.
⚠ Cognitive biases affecting performance ratings.
⚠ Data quality and visibility issues with spreadsheet-based approaches.
⚠ Scheduling and coordination complexity for calibration sessions.
⚠ Documentation and audit trail gaps for calibration decisions.
⚠ Reliance on manual processes can lead to errors and inefficiencies.
⚠ Inadequate documentation can hinder transparency and accountability.

Future State

(Agentic)

1. Calibration Agent pre-analyzes ratings before calibration session: identifies outliers (manager rating all employees 4.5+), inconsistencies (similar employees rated differently), and forced distribution impacts. 2. Agent provides data-driven calibration recommendations: 'Sarah and Bob have similar goal achievement (90% vs 88%) and 360 scores (4.3 vs 4.2) - recommend both rated 4.5 vs Sarah 4.5 and Bob 4.0'. 3. Agent reduces calibration meeting to 2-3 hours (vs 8-12 hours) by pre-flagging only cases needing discussion (20-30% of employees). 4. Agent performs pay equity analysis: 'Women in Engineering rated 4.2 avg receiving 3.0% merit vs men rated 4.2 receiving 3.5% merit - investigate pay disparity'. 5. Agent models pay-for-performance scenarios: 'Current merit budget of 3.5% applied to ratings yields 2.5% merit for meets expectations, 4.5% for exceeds - does this align with philosophy?'. 6. Agent flags compensation anomalies: 'John rated top performer but paid at market 50th percentile - retention risk, recommend adjustment'.

Characteristics

• Manager-submitted performance ratings and justifications
• Goal achievement, 360 feedback, continuous feedback data
• Employee compensation history and current salary
• Market compensation benchmarks by role and location
• Pay equity data (gender, race, tenure, demographics)
• Merit budget and compensation philosophy guidelines
• Historical calibration decisions and rating distributions

Benefits

✓ 70-85% time savings (2-3 hours vs 8-12 hours) through AI pre-analysis
✓ Rating consistency improved through data-driven recommendations
✓ Pay equity analysis ensures fair compensation across demographics
✓ Pay-for-performance modeling aligns merit increases with ratings philosophy
✓ Anomaly detection identifies retention risks (high performers underpaid)
✓ Manager satisfaction improved (less time, more fair outcomes)

Is This Right for You?

39% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

• Applicable across multiple industries
• Higher complexity - requires more resources and planning
• Moderate expected business value
• Time to value: 3-6 months
• (Score based on general applicability - set preferences for personalized matching)

You might benefit from Calibration & Compensation Alignment if:

You're experiencing: Time and resource intensity of the calibration process.
You're experiencing: Process fragmentation leading to inconsistencies across teams.
You're experiencing: Cognitive biases affecting performance ratings.