UC34: Workforce Planning & Attrition Prediction

The Problem

80% of organisations will use AI for workforce planning by 2025. Yet most still react to turnover after it happens. Proactive attrition prediction identifies flight risk employees early (months before they resign), allowing targeted retention interventions (promotion, raise, development opportunity). Key indicators of flight risk include: stagnant role tenure (2+ years no promotion), engagement survey signals (low response, negative themes), compensation below market, unusual behaviour (less active in meetings, less collaboration), and market conditions (team skills in high demand). Identifying at-risk employees allows HR to intervene before valuable talent is lost.

What the Agent Does

Analyses turnover patterns: retrieves historical termination data (past 2 to 3 years), disaggregates by department, role, tenure, manager; identifies high-turnover units (e.g., "Sales team loses 30% annually"; engineering loses 10%)
Analyses engagement signals: retrieves engagement survey data (Qualtrics, Culture Amp), identifies employees with negative sentiment or disengagement; correlates engagement scores with subsequent turnover (employees with low engagement score are X times more likely to leave)
Monitors compensation competitiveness: compares internal salary data against market (PayScale, Radford, Mercer survey data); identifies underpaid employees (employees paid >20% below market are flight risk)
Detects behaviour changes: analyses employee activity logs if available (email/calendar activity, project participation, collaboration patterns); identifies changes in activity (decrease in calendar invites, less participation in meetings, reduced collaboration) that may signal disengagement
Builds attrition risk model: combines signals (engagement score, tenure, compensation gap, behaviour changes, market demand for skills) into risk score (0 to 100 scale); flags employees >60 as "high risk", >75 as "critical risk"
Recommends interventions: for high-risk employees, recommends targeted actions: (1) promotion opportunity, (2) raise to market, (3) development/rotational assignment, (4) manager discussion/check-in
Generates workforce planning report: current turnover rates by department, predicted attrition risk (number of high-risk employees), recommended interventions, estimated impact (if interventions implemented, expected reduction in turnover)
Tracks intervention outcomes: monitors whether recommended interventions were implemented; measures whether interventions reduced attrition (did employee stay if promoted/raised?)

Data Requirements

Data Sources:

HRIS (Workday, SuccessFactors, ADP) , employee tenure, promotion history, compensation, termination data
Engagement survey data (Qualtrics, Culture Amp, CultureWizard) , engagement scores, survey responses, sentiment
Compensation survey data (PayScale, Radford, Mercer, Aon) , market salary comparisons
Exit interview data (if available) , reasons employees left
Manager feedback (optional, if collected systematically) , manager assessment of engagement
Employee activity/collaboration data (optional) , email activity, calendar participation, collaboration tool activity (Slack, Teams, Jira)
Market data (if available) , job posting volume, salary trends for specific skills (indicates market demand)

Data Classification:

Public: Aggregated turnover trends, general workforce planning insights
Internal Structured: Employee tenure, promotion history, engagement scores, compensation data
Internal Unstructured: Engagement survey free-text responses, manager feedback
Sensitive/Regulated: Individual attrition risk scores (should be handled confidentially), engagement data

Data Quality Requirements:

Employee tenure and promotion history must be accurate in HRIS
Engagement survey data should be complete (>70% response rate for trend analysis)
Compensation data must be current as of latest pay period
Termination reasons should be recorded (helps correlate with risk factors)

Integration Complexity: Medium to High , Requires HRIS API, engagement survey API, compensation survey data (may require manual upload), and optional integration with collaboration/activity tools. Attrition prediction model building requires machine learning expertise or pre-built models. Integration is 6 to 8 weeks if building custom model; 3 to 4 weeks if using pre-built model.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	3	0.45
Error Reduction	10%	3	0.30
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	5	0.25
Data Availability	15%	3	0.45
Process Clarity	15%	2	0.30
Ease of Implementation	10%	2	0.20
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.80

Why It Scores Well

Workforce planning and attrition prediction is a strategic, data-driven capability. The agent identifies at-risk employees systematically (saves 20 to 30 hours/quarter of manual analysis), enables proactive retention interventions (prevents turnover of high-value talent), and supports strategic workforce planning. Process is repeatable and model improves over time with more data.

Regulatory Alignment

No specific regulatory requirements for attrition prediction, but retention interventions should not discriminate based on protected characteristics (race, gender, disability); monitor that risk scores don't disproportionately flag certain demographic groups

Sprint Factory Fit

Sprint 3 (2 weeks) + 2 build sprints (4 weeks)

Scores 3.80. High-value use case: (1) enables proactive attrition management (saves valuable talent from being lost), (2) provides strategic workforce planning insights (supports succession planning, resource planning), (3) process is systematic and repeatable (model runs regularly and improves), and (4) data is available in HRIS and engagement systems. Integration and model building are moderately complex. Business impact is strategic and high (retention of high-value talent, strategic planning).

Comparable Implementations

KPMG: Strategic workforce planning with AI agents , AI-driven attrition prediction and workforce planning
Gloat: Agentic AI in HR and talent mobility , skills-based workforce planning and retention insights
LinkedIn Talent Solutions: Workforce planning and analytics , labour market data and attrition benchmarking

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