Why humans still matter in AI-driven HR and payroll

Author: Jonathan Aitken

AI and automation can speed up HR and payroll dramatically, capturing data, checking policy rules, detecting anomalies, and generating reports in seconds. But these systems still lack organisational context, ethical judgment, and legal accountability. A structured human-in-the-loop (HITL) design is therefore essential, especially for South African employers operating under the BCEA, LRA, SARS (PAYE/ETI/EMP201/EMP501/IRP5), UIF/SDL, Employment Equity, and POPIA requirements. The goal isn’t to slow automation down; it’s to make it safe, compliant, and trusted.

Here. we explore various aspects of the HTIL as it relates to AI processes, and consider an implementation timeline for AI processes to help build the right level of human interaction and training. As a good rule of thumb, when the human team involved in the process no longer knows what happens under the hood, and why their input is critical to a successful process, then the process is failing. Ultimate accountability must sit with the human team.

What “human-in-the-loop” means (in HR and payroll)

A HITL process deliberately places human decision points at stages where:

• Legal interpretation or ethical judgment is required
• The data is ambiguous or impacts people’s livelihoods or rights
• Exceptions, high value, or high risk are present
• Model outputs must be explained, defended, or audited

Practically, this looks like maker–checker controls, approvals, exception queues, and post-run quality checks, all captured in an audit trail.

Where AI/automation excels

• Data capture and validation: OCR of IDs, contracts, medical aid forms, union rule checks, SARS number format validation, and banking detail verification
• Policy engines: Validating overtime, leave, shift premiums, allowances, and deductions against company policy/collective agreements
• Anomaly detection: Flagging net pay spikes, new bank accounts, ghost employees, out-of-band overtime, and ETI eligibility inconsistencies
• Classification and drafting: Categorising HR tickets, summarising grievances, and drafting letters, job ads, or interview notes for review
• Monitoring and reporting: Variance analysis to prior periods, headcount/fixed–variable cost dashboards, and EE plan progress checks

Where human judgment is non-negotiable

• Legal interpretation and edge cases: BCEA deductions (s34), complex leave accruals, retrenchment calculations, notice and severance, sectoral determinations, cross-border tax residency, and ETI edge cases
• Fairness and bias: Screening candidates, promotions, and performance signals, avoiding discriminatory proxies, and ensuring equity
• Employee relations: Misconduct, incapacity, grievance outcomes, and disciplinary sanctions
• Payments and terminations: Final pay, garnishees, third-party payments, and bank file releases
• Privacy and ethics: POPIA lawful basis, data minimisation, and purpose limitation for any model training or analytics

The risk if you remove human review

• Regulatory breaches: Incorrect PAYE/UIF/SDL, ETI mis claims, EMP501 recon breaks, and non-compliant EE submissions
• Financial loss: Over/under-payments, penalties, duplicate payments, and fraud
• People harm: Unfair treatment, wrongful deductions, and morale/retention damage
• Reputation and legal exposure: POPIA violations, grievance escalation, and union disputes
• Model drift and silent failure: Policies change – models don’t notice unless a person does

A practical HITL control framework

Governance

• Policy: Written automation policy covering scope, decision rights, and POPIA compliance
• RACI: Clear accountability for each stage (HR, Payroll, Finance, IT, Info Officer)
• Change control: Versioning for rules/models, documented approvals, and rollback plans

Process controls

• Maker–checker: Dual control for master data changes (bank accounts, rates), payroll inputs, and payment files
• Tiered reviews:
  • Auto-pass for low-risk items
  • Human confirmation for medium-risk anomalies
  • Senior sign-off for high-value or termination cases
• Exception queues: Time-boxed SLA to resolve AI flags before cut-off
• Sampling and QA: Random and risk-weighted post-pay run checks, and quarterly deep-dives
• Explainability gate: For any LLM/ML decision used in HR, require a human-readable rationale before action
• Audit trail: Immutable logs of inputs, prompts, model versions, overrides, and approvals (retain for at least five years for SARS)

Security and privacy (POPIA)

• Minimise personal data in prompts and training sets, mask IDs
• Role-based access, least privilege for bots and humans
• Data processing agreements with vendors, cross-border safeguards

Design patterns that work

1. Pre-payroll “green bar” check
   • AI proposes a variance report (current vs prior month) with outliers
   • Payroll Manager reviews top exceptions (>R5,000 variance, new bank accounts, ETI eligibility flips)
   • Finance signs off before bank file creation
2. Master data change with dual control
   • Employee or HR requests change
   • Bot validates documentary proof and flags suspicious patterns
   • Payroll Admin updates, Payroll Manager approves, and system logs both
3. Leave and overtime approval
   • Bot validates rules (limits, public holidays, shift codes)
   • Line Manager confirms context (project deadlines, emergency work)
   • HRBP spot-checks by risk score (excessive OT, chronic patterns)
4. Termination calc workflow
   • Engine drafts pro-rata leave, notice, severance, deductions
   • HR reviews fairness and BCEA compliance
   • Finance authorises payment
   • Legal reviews when risk flags appear
5. Recruitment screening assist
   • AI summarises CVs to a structured rubric
   • Human panel scores against job-related criteria only, DEI bias checks
   • Decisions documented with reasons

Minimal RACI (illustrative)

Metrics that prove it’s working

• Payroll defects per 1,000 payslips
• % exceptions cleared before cut-off
• False-positive / false-negative rates on AI flags
• Time-to-resolve exceptions
• % automated with human oversight (by risk tier)
• Employee trust indicators (ticket volume on pay queries, survey scores)
• Regulatory KPIs (SARS/DoEL submissions on time, zero recons breaks)

30-60-90 Day rollout

 Days 0–30: Foundations

• Map processes, identify high-risk decisions
• Define review tiers, SLAs, and RACI
• Stand up logging, access controls, and POPIA safeguards

Days 31–60: Pilot

• Automate inputs, variance analytics, and exception queues for one payroll group
• Train reviewers, capture overrides with reasons
• Calibrate thresholds, measure error rates

Days 61–90: Scale and harden

• Expand to terminations, master data changes, and third-party payments
• Add quarterly model/rules review board
• Integrate signoffs into your GRC/audit platform

Quick guide: Do / Don’t

 Do

• Keep humans on the hook for payments, terminations, and legal outcomes
• Require explanations before action
• Document every override and approval

Don’t

• Let an AI send a bank file or reject a candidate without a human
• Feed sensitive personal data into prompts without masking
• Assume models will keep up with law or policy changes

Automation should make HR and payroll more human, not less, freeing people to exercise judgment, empathy, and stewardship while machines handle the repetitive work. A well-designed human-in-the-loop approach gives you the best of both worlds: speed with accountability, efficiency with fairness, and innovation with compliance.

Our team is here to help! Contact HRTorQue today should you need to reassess or revisit your HR and payroll processes.