K
EU AI Act: Not High Risk Q2

Compensation Benchmarking Agent

Market data meets internal equity - compensation analysis without spreadsheet chaos.

Analyses market data and internal pay structures, maintains compensation bands, and delivers data-driven foundations for compensation strategy.

Analyse your process
Airbus Volkswagen Shell Renault Evonik Vattenfall Philips KPMG

Assessment

Agent Readiness 68-75%
Governance Complexity 36-43%
Economic Impact 61-68%
Lighthouse Effect 51-58%
Implementation Complexity 38-45%
Transaction Volume Quarterly

What This Agent Does

Compensation decisions require two types of data: where you stand internally (pay equity, band adherence, compa-ratios) and where you stand externally (market positioning by role, level, and geography). Most organisations assemble this picture manually, combining survey data with internal exports in spreadsheets - a process that is slow, error-prone, and outdated the moment it is complete. The Compensation Benchmarking Agent automates this assembly. It ingests internal compensation data from payroll and HR systems, maps roles to external survey benchmarks using standardised job architecture, calculates key metrics (compa-ratio, range penetration, internal equity ratios), and produces the analysis that compensation committees and HR business partners need to make informed decisions. Critically, this agent analyses and reports - it does not decide. Compensation decisions remain with human decision-makers. The agent's value is in making the data available faster, more accurately, and more consistently than manual assembly allows.

Micro-Decision Table

Human
Rules Engine
AI Agent
Each row is a decision. Expand to see the decision record and whether it can be challenged.
Collect internal compensation data Extract current pay data per employee, role, and level AI Agent

Automated data extraction with anonymisation where required

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Map roles to benchmarks Match internal job titles to external survey job families AI Agent

AI-assisted matching with human validation for ambiguous mappings

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Validate benchmark mapping Confirm or correct AI-suggested role-to-benchmark matches Human

Human review ensures correct job matching for fair comparison

Decision Record

Decider ID and role
Decision rationale
Timestamp and context

Challengeable: Yes - via manager, works council, or formal objection process.

Calculate internal metrics Compute compa-ratios, range penetration, equity ratios Rules Engine

Deterministic calculations per defined formulas

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Identify outliers Flag positions significantly above or below market or internal norms AI Agent

Statistical outlier detection based on configurable thresholds

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Generate benchmarking report Produce analysis in required format for decision-makers AI Agent

Automated report generation with visualisations and data tables

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Distribute to authorised users Share report with defined recipient list Rules Engine

Access controls based on compensation data sensitivity classification

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Decision Record and Right to Challenge

Every decision this agent makes or prepares is documented in a complete decision record. Affected employees can review, understand, and challenge every individual decision.

Which rule in which version was applied?
What data was the decision based on?
Who (human, rules engine, or AI) decided - and why?
How can the affected person file an objection?
How the Decision Layer enforces this architecturally →

Prerequisites

  • Standardised job architecture (job families, levels, grades)
  • External compensation survey subscriptions (Mercer, Radford, WTW, or equivalent)
  • Internal compensation data from payroll and HR systems
  • Defined pay ranges per grade and location
  • Data anonymisation rules for individual-level analysis
  • Access control framework for compensation data

Governance Notes

EU AI Act: Not High Risk
Not classified as high-risk under the EU AI Act - the agent analyses data without making employment-affecting decisions. However, the EU Pay Transparency Directive (2023/970) creates new obligations for pay reporting that this agent directly supports. GDPR applies to individual-level compensation data processing. Aggregated reports used for pay gap analysis must follow the directive's methodology requirements. Works council information rights may apply where compensation data analysis is considered employee monitoring.

Infrastructure Contribution

The Compensation Benchmarking Agent builds the job-to-benchmark mapping and pay range infrastructure that the Merit Cycle Governance Agent and Promotion Process Agent require. Without standardised benchmarking data, neither merit allocation nor promotion recommendations can be grounded in market reality. Builds Decision Logging and Audit Trail used by the Decision Layer for traceability and challengeability of every decision.

Does this agent fit your process?

We analyse your specific HR process and show how this agent fits into your system landscape. 30 minutes, no preparation needed.

Analyse your process

What this assessment contains: 9 slides for your leadership team

Personalised with your numbers. Generated in 2 minutes directly in your browser. No upload, no login.

  1. 1

    Title slide - Process name, decision points, automation potential

  2. 2

    Executive summary - FTE freed, cost per transaction before/after, break-even date, cost of waiting

  3. 3

    Current state - Transaction volume, error costs, growth scenario with FTE comparison

  4. 4

    Solution architecture - Human - rules engine - AI agent with specific decision points

  5. 5

    Governance - EU AI Act, works council, audit trail - with traffic light status

  6. 6

    Risk analysis - 5 risks with likelihood, impact and mitigation

  7. 7

    Roadmap - 3-phase plan with concrete calendar dates and Go/No-Go

  8. 8

    Business case - 3-scenario comparison (do nothing/hire/automate) plus 3×3 sensitivity matrix

  9. 9

    Discussion proposal - Concrete next steps with timeline and responsibilities

Includes: 3-scenario comparison

Do nothing vs. new hire vs. automation - with your salary level, your error rate and your growth plan. The one slide your CFO wants to see first.

Show calculation methodology

Hourly rate: Annual salary (your input) × 1.3 employer burden ÷ 1,720 annual work hours

Savings: Transactions × 12 × automation rate × minutes/transaction × hourly rate × economic factor

Quality ROI: Error reduction × transactions × 12 × EUR 260/error (APQC Open Standards Benchmarking)

FTE: Saved hours ÷ 1,720 annual work hours

Break-Even: Benchmark investment ÷ monthly combined savings (efficiency + quality)

New hire: Annual salary × 1.3 + EUR 12,000 recruiting per FTE

All data stays in your browser. Nothing is transmitted to any server.

Compensation Benchmarking Agent

Initial assessment for your leadership team

A thorough initial assessment in 2 minutes - with your numbers, your risk profile and industry benchmarks. No vendor logo, no sales pitch.

30K120K
1%15%

All data stays in your browser. Nothing is transmitted.

Related Pages

Decision Layer
Governance layer between AI agent and target system. Every decision documented and challengeable.
Co-Determination & AI
Works council, works agreements, and co-determination as architecture constraint.
HR AI Agents
All HR agent services at a glance.
HR Agent Readiness
7 questions, 3 minutes. How ready is your organisation?
Assessment eBook
15 questions for your leadership team as PDF.

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Back to Catalog

Frequently Asked Questions

Does the agent recommend specific salary amounts?

No. The agent provides analysis - market positioning, compa-ratios, equity metrics, and outlier flags. Compensation decisions are made by human managers and compensation committees using this data as one input among several.

How current is the market data?

The agent integrates with your compensation survey subscriptions and updates benchmarks when new survey data is published. The refresh frequency depends on your survey providers - typically annually for comprehensive surveys, with quarterly or real-time updates for some data sources.

What Happens Next?

1

30 minutes

Initial call

We analyse your process and identify the optimal starting point.

2

1 week

Discover

Mapping your decision logic. Rule sets documented, Decision Layer designed.

3

3-4 weeks

Build

Production agent in your infrastructure. Governance, audit trail, cert-ready from day 1.

4

12-18 months

Self-sufficient

Full access to source code, prompts and rule versions. No vendor lock-in.

Implement This Agent?

We assess your process landscape and show how this agent fits into your infrastructure.