NBA evidence challenges common assumptions about talent management: top performers do not necessarily become strong leaders, high status can reduce the additional rewards of success, and recent success may even weaken subsequent performance. Organizations should therefore evaluate employees based on future role requirements, actual contribution, and sustained evidence rather than relying on reputation or past results. Article 024

Managers often judge foreign markets not by actual institutional differences, but by how different or familiar those markets appear. Underestimating differences can encourage poorly prepared expansion, while exaggerating them can cause firms to reject viable opportunities. Better decisions require cognitive flexibility, systematic evidence, and treating familiarity as a hypothesis rather than a fact.

Sustainability is evolving from a compliance obligation into a driver of enterprise value, innovation, and competitive advantage. Yet a significant valuation gap remains between executives’ awareness and their ability to quantify financial impact. Closing this gap requires stronger measurement frameworks, financial tools, sustainability skills, and integration into strategy, investment, and entrepreneurship.

AI medical devices are entering healthcare far faster than the evidence supporting their safety and effectiveness. Limited demographic reporting, uneven validation, cybersecurity gaps, and potential algorithmic bias mean regulatory approval alone is insufficient; health systems need rigorous local evaluation to ensure AI works safely and equitably for the populations they serve.

Clean air policy is fundamentally a child health intervention, as air pollution worsens asthma and can limit lifelong lung development. Evidence shows that stringent clean air measures can improve children’s lung function within years, particularly among disadvantaged groups, while generating wider economic benefits through lower healthcare costs, improved productivity, and better long-term health.

Generative AI performs strongly with structured clinical data but struggles to interpret the informal, culturally diverse language patients use to describe symptoms, creating risks for safety, efficiency, liability, and health equity. Successful deployment requires real-world validation, human oversight, continuous monitoring, and patient-centered interfaces designed around natural communication.