Manual skills surveys age quickly and annoy staff. This talk shows a hybrid approach: machine‑learning models scrape HRIS, LMS, code repositories and project tickets to infer skills and proficiency, while targeted human validation ensures precision. We’ll cover model selection, confidence scoring, GDPR‑compliant consent flows and the change‑management tactics that drive adoption.

Data pipelines: source systems and feature engineering

Model confidence thresholds and triage rules

Human‑validation workflows and crowd endorsements

Privacy‑by‑design consent language

Continuous‑learning loop for model improvement.

• Select data sources that maximise inference quality

• Design governance that balances automation speed with accuracy

• Craft consent and transparency practices that satisfy GDPR

• Implement feedback loops to keep skills data fresh.

Organisations waste budget and lose credibility when inventory data is incomplete or outdated; investors and auditors now ask how “real” the data is. AI inference promises coverage but must be explainable and lawful.