London Business School (institutional)

Can AI Make Better People Decisions than Humans?

The Think Ahead podcast episode examines whether artificial intelligence can out‑perform human intuition in people‑related decisions, focusing on hiring, promotion, and retention. Professors Isabelle Fernandez Mateo and Sergey Gurif, together with Hatti Sundaram of the data‑driven hiring platform Applied, discuss the shift from gut‑feel judgments to evidence‑based approaches powered by richer data and advanced machine‑learning models. Key insights include the explosion of granular employee data—from application details to digital communication footprints—and the recognition that traditional intuition is riddled with cognitive biases such as first‑impression anchoring and similarity attraction. The guests argue that structured interview protocols, clear competency rubrics, and longitudinal tracking of hires provide more reliable predictors of future performance than unstructured “chemistry” conversations. Illustrative quotes highlight the tension: Sundaram notes that “good data plus bad data equals noisy decisions,” while Fernandez Mateo warns that “correlation is not causation; selection bias can mislead even sophisticated AI models.” Real‑world examples, such as the “airplane test,” reveal how organizations attempt to blend experiential assessments with data but often fail to validate outcomes over six‑month or twelve‑month horizons. The implications are clear: firms must invest in systematic post‑hire analytics, align incentives with long‑term performance metrics, and train managers to critically interrogate AI outputs. Without these safeguards, the promise of AI‑enhanced hiring risks perpetuating existing biases rather than eliminating them.