HR & People

Attrition risk prediction

Predict which employees are at risk of leaving based on engagement signals, tenure patterns, role changes, and compensation data. Enable proactive retention interventions.

Decision supportPattern-matchingCost reductionAccuracy

15–30%

reduction in unwanted attrition

Opportunity assessment

Business Impact
2

Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.

Feasibility
2

Challenging. Significant technical work required. Likely needs an AI engineer or specialist vendor. Not a quick win.

Data Readiness
2

Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.

Risk Exposure
3

Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.

Change Complexity
2

High people impact. Daily workflows change materially for a number of people. Training, communication, and active change management needed.

Tooling required

Specialist AI toolFine-tuned model

Things to consider

  • This requires sufficient historical data on who has left, when, and the signals that preceded departure. Most businesses have the data but it's spread across HRIS, engagement surveys, and manager records.

  • The ethical and trust implications are significant. If employees learn they're being scored for flight risk, it changes the employer-employee relationship. Be transparent about what data is used.

  • Prediction without action is pointless. The model must be paired with a clear retention playbook — what interventions are available, who authorises them, and what budget exists.

  • Experiment starter: Retrospectively analyse the last 50 voluntary leavers. Map their tenure, role changes, compensation history, and engagement scores against a control group who stayed. Identify the three strongest predictors of departure. If clear patterns exist, a predictive model will work.

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