Show Notes
Most executive decisions are forecasts in disguise. 'Will this market grow?' 'Will this AI initiative deliver ROI?' 'Will this acquisition integrate well?' These are all probability estimates — and most leadership teams make them without any structured method, relying instead on experience, intuition, and the confidence of whoever speaks loudest in the room.
In this episode, Kenza and Pascal explore two landmark studies on AI-augmented forecasting and what they mean for executive decision-making. The first, by Halawi et al. (NeurIPS 2024), shows that a retrieval-augmented AI system can approach the forecasting accuracy of competitive human forecasters — and that combining AI and human judgment always outperforms either alone. The second, by Schoenegger et al. (2025, co-authored with Philip Tetlock, the world's leading expert on forecasting), shows that AI assistance improves human forecasting accuracy by 24–28% — even when the AI assistant is deliberately biased and overconfident.
The key insight: the benefit is not just informational. It is cognitive. Engaging with an AI perspective — even an imperfect one — forces assumption-surfacing, reduces overconfidence, and makes human judgment measurably more accurate. The Brier Score — a rigorous calibration metric that measures how well-calibrated probability estimates are, not just whether someone was right or wrong — confirms the improvement is real and quantifiable.
For boards making high-stakes decisions under uncertainty, this is not a hypothetical. It is a demonstrated, quantifiable capability that most executive teams are not yet using systematically.
Key Takeaways