Leadership Science in the Age of AI: From Data to Dividends

Evidence-based leadership that moves P&L and wins CFO approval
Jig Ramji Rosaria Bonifacio
Jig Ramji, Roffey Park Institute
Rosaria Bonifacio, Nokia

Available to:

Leadership science has matured from static competency lists to dynamic, AI-driven predictors tied directly to revenue. In this fast-paced episode, Jig Ramji and host Rosaria Bonifacio unpack the ‘One LSEG’ model, Bloomberg’s predictive framework, and the dashboards that finally satisfied the CFO. They surface governance moves that pushed analytics beyond Power BI, wrestle with algorithmic bias, and share personal data habits that keep leaders accountable. Listeners leave with numbers, executive-ready governance tactics, and a checklist to monetise leadership data across the enterprise.

Growth targets, market volatility, and AI disruption have forced boards to treat leadership quality as a financial asset, yet most HR metrics still fail to sway capital planners. LSEG’s transformation and Bloomberg’s talent experiments show why linking leadership indices to margin, capital efficiency, and growth unlocks investment and competitive advantage across industries.

This session will explore:

  • Translating the ‘One LSEG’ ambition into a measurable leadership index and avoiding common traps.
  • Using predictive analytics at Bloomberg to isolate high-growth competencies and retire outdated assumptions.
  • Quantifying the financial effect size of leadership quality and building CFO-ready dashboards.
  • Engineering governance so people data flows into enterprise lakes and cross-functional decisions.
  • Balancing speed and rigour in AI-powered assessments while guarding against bias.
  • Distinguishing skills gaps from leadership growth constraints and tracking hybrid-world competencies.

Learning outcomes:

  • Benchmark leadership quality against financial metrics using effect-size calculations that satisfy finance leaders.
     
  • Design predictive competency models that outperform static frameworks and surface unexpected success variables.
     
  • Build governance and data-engineering pipelines that integrate people analytics into enterprise decision rights.
     
  • Establish evidence thresholds for safe AI use in promotion, succession, and hybrid-team contexts.
     
  • Equip HR business partners with change tactics that shift mindsets from reports to insight.