AI in CompTech: Vendor Research Guide
This research starts from the compensation use cases that matter most to practitioners, then evaluates how AI helps solve them. Use it to prepare demonstrations grounded in real workflow outcomes, not AI for AI's sake.
Research Foundation
Research Purpose and Approach
The use cases driving this research
Compensation teams face growing pressure to run planning cycles faster, detect equity risks earlier, price roles with thinner data, and link reward decisions to business outcomes. This research examines how AI embedded in CompTech modules addresses those pressures today and what is on the near-term roadmap.
How we'll assess what good looks like
We start from desired outcomes, not AI features. Through scenario-based demos we test whether AI actually helps practitioners make faster, better-defended decisions, with governance integrated into the workflow rather than bolted on as a separate layer.
Methodology
Your Demo Checklist
Showing real system behaviour rather than slide decks
01
Run complete workflows
From data input to final decision, not slide decks.
02
Show how AI shapes each step
Explain your reasoning at each decision point.
03
Demonstrate governance in action
Show overrides, explanations, and audit trails working, not just in menus.
Do
  • Pre-configure demo environments with representative data.
  • Use sample datasets mirroring typical customer inputs.
  • Prepare user personas with appropriate permissions.
  • Seed AI models with realistic compensation data.
Don't
  • Show only cherry-picked examples.
  • Present hardcoded outputs as AI predictions.
  • Offer "Coming soon" features as available.
  • Use mockups or prototypes not in production.
Evaluation Scope
Use-Case Scenarios and Evaluation Scope
In Scope
  • AI capabilities evaluated through four core use-case scenarios: compensation planning, pay equity analysis, market pricing, and market data insights
  • Governance capabilities: explainability, confidence signalling, human oversight, audit trails
  • Integration patterns with HRIS, survey providers, and analytics platforms
  • Workflow automation and decision support (chatbots, copilots, agents)
Scored Elements
  • Whether AI helps practitioners reach faster, better-defended decisions in each use case
  • Transparency of AI logic and explainability features
  • Governance controls and auditability mechanisms
  • Handling of uncertainty, edge cases, and data quality issues
Out of Scope
  • Core CompTech functionality without AI components
  • Pricing, contract terms, or implementation timelines
  • Non-AI workflow automation (rules engines, scheduled reports)
  • Customer success processes or support models
  • Build-vs-buy and DIY tooling comparisons (planned as a separate research workstream)
Not Scored
  • UI/UX design aesthetics
  • Brand positioning or marketing narratives
  • Number of AI features (quality over quantity)
  • Technology stack or architectural choices
Tenet 1
Explainability: Making AI Reasoning Transparent
The system must articulate why it made a specific recommendation in terms a (non-technical) comp professional can understand and defend. Explanations must be actionable and accessible at the point of decision, not buried in documentation. Use-case sensitivity: Explainability carries the highest weight in pay equity analysis, where every recommendation must withstand legal and employee-relations scrutiny. In market pricing it matters most when survey data is sparse and the system must justify proxy matches.
Good Looks Like
  • Visible data inputs driving recommendations with specific figures and sources
  • Feature importance shown (e.g., location contributed 35%, job level 25%)
  • Actionable explanations where users can adjust inputs and see output changes
  • Non-technical users can understand logic without data science training
Bad Looks Like
  • Recommendations with no supporting rationale
  • Explanations requiring SQL queries or API calls to access
  • Technical jargon incomprehensible to end users
  • Explanations only available to admins, not end users
  • Circular logic providing no real insight
Tenet 2
Confidence: Signal Reliability, Show Uncertainty, and Set Guardrails
You need to clearly communicate your level of confidence. When you're sure, say so. When you're not, acknowledge it by showing uncertainty with confidence intervals or ranges, and by stating when data is insufficient. You also need to acknowledge your limitations, especially when data is sparse or outside your training domain. Most importantly, you must include guardrails to prevent illegal outputs, policy violations, and nonsensical recommendations. Use-case sensitivity: Confidence is critical in market pricing and benchmarking, where thin survey samples and proxy matches are common. The system must surface how certain it is about each data point so practitioners know when to trust the output and when to apply professional judgement.
1
Confidence Intervals
Provide predictions with probability ranges (e.g., "£80K-£90K with 90% confidence") instead of false precision.
2
Data Quality Flags
Flag when data is sparse, outdated, or conflicting to prevent overconfident recommendations.
3
Policy Guardrails
Prevent recommendations that violate constraints (e.g., no offers below minimum wage or above budget caps).
4
Graceful Degradation
When data is insufficient, state "not enough information to recommend" rather than guessing.
5
Historical Accuracy
Display visible metrics showing model performance (e.g., "predictions within 5% of actual hires in 78% of cases").
Tenet 3
Human-in-the-Loop: Augment Human Decisions
Compensation decisions carry significant financial and legal implications, directly impacting employee morale, retention, and overall business performance. Therefore, human judgment, ethical consideration, and ultimate authority must remain central to any AI-assisted process. Use-case sensitivity: Human-in-the-loop controls are non-negotiable in compensation planning and pay equity, where individual employee outcomes are at stake. In market data and insights, lighter-touch oversight may suffice for aggregate reporting, but any recommendation that feeds a pay decision must have a clear human sign-off point.
Human-in-the-Loop
AI must augment human decision making, not replace it. Humans retain authority, oversight, and the ability to override recommendations with proper justification and logging.
Clear Handoff Points
AI provides data-driven salary benchmarks, bonus pool allocations, or equity grant recommendations, but the final decision and contextual application (e.g., considering an employee's specific trajectory or team dynamics) are made by compensation professionals with transparent workflow transitions.
Override Capabilities
Compensation specialists must have the ability to adjust or reject AI recommendations, especially for exceptional cases, critical retention scenarios, or to ensure internal equity. Any deviation from the AI's suggestion requires documented justification, which is logged for transparency, future audits, and model improvement.
Role-Based Access
Access to AI-generated compensation insights and the ability to modify parameters or recommendations are tailored to specific roles. This ensures that HR generalists, compensation analysts, and senior leadership each have appropriate levels of visibility and permission, safeguarding sensitive data and maintaining proper governance over compensation policy.
Tenet 4
Auditability: Traceable Decision Records
Every AI-influenced compensation decision requires a complete, traceable record. Use-case sensitivity: Auditability is paramount in pay equity analysis, where decisions may need to be defended to regulators, works councils, or in litigation. In compensation planning, audit trails support governance sign-off and budget reconciliation. Even in market data modules, logging which sources, cuts, and ageing factors were applied is essential for reproducibility.
Comprehensive Logging
Log all data inputs, model versions, recommendations, human decisions, timestamps, and user IDs. This creates a full, meticulous audit trail.
Decision Reconstruction
Any AI recommendation must be fully reconstructible. Explain any decision with complete context, even after months, by referencing the logged data and model state.
Exportable Records
Audit logs and decision records must be easily accessible and exportable. Use standard, machine-readable formats like CSV or JSON for legal and compliance review.
Module Testing
Use-Case Scenarios and Evaluation Criteria
Each scenario below is anchored in a real compensation workflow outcome. We will assess both what is in production today and what is on the near-term product roadmap, so vendors should be prepared to demonstrate live functionality and walk us through pipeline features.
Compensation Planning
Merit increase recommendations, promotion modelling, budget allocation optimisation, and planning copilots for what-if scenarios.
Pay Equity Analysis
Unexplained pay gap detection, intersectional equity analysis, remediation scenario modelling, and continuous monitoring with anomaly detection.
Market Pricing
Predictive market pricing for sparse data roles, real-time market movement detection, job matching AI, and chatbots for salary queries.
Market Data Insights
Anomaly detection in survey submissions, predictive analytics on market trends, data quality scoring, and intelligent data enrichment.
Demo Protocol
Interactive Demo Session Structure
1
Phase 1: Baseline Demonstration
30 minutes: Vendors walk through prepared use-case scenarios showing how AI helps practitioners reach better compensation decisions faster. Focus on workflow outcomes, not feature tours.
2
Phase 2: Interactive Probing
30 minutes: We pose practitioner-style problems drawn from the four use-case scenarios on the previous card. We are looking for how the system handles real-world complexity, not rehearsed demos.
3
Phase 3: Edge Case and Stress Testing
20 minutes: Deliberately challenging inputs, such as incomplete data, conflicting constraints, or policy edge cases, test whether the system degrades gracefully and communicates uncertainty honestly.
4
Phase 4: Wrap-up and Q&A
10 minutes: Open discussion and final questions.
Explainability Probes
"Why did the AI recommend this?" "Which data point had the largest influence?" "Show me the calculation."
Confidence Probes
"What happens if I remove 50% of inputs?" "How confident is the system?" "What would change certainty?"
Human-in-the-Loop Probes
"Can managers override this?" "What happens if someone ignores AI?" "How do non-technical users know when to trust output?"
Auditability Probes
"Can you reconstruct this decision in 6 months?" "Show me the audit trail." "How do you track model versions?"
Session timing: We recommend approximately 60 minutes, but this can flex depending on how extensive the vendor's AI coverage is. A vendor demonstrating a single AI feature within one module will need less time than one showcasing AI applications across the full platform. We are happy to adjust.