As organisations move AI beyond experimentation, success depends less on model accuracy and more on ownership, governance, and accountability. This session explores how enterprises transition from isolated data science projects to structured, outcome-driven AI programmes with clear responsibilities, shared standards, and measurable impact.

 

Drawing on real-world case studies – including failures and course corrections – Iris and Richa outline practical frameworks for scaling AI responsibly: defining ownership through RACI models and data contracts, aligning different stakeholder teams, and building reusable analytics products instead of one-off solutions. The talk also addresses how to balance experimentation with accountability using fail-fast principles, KPI design, and ROI-driven prioritisation.

 

Attendees will leave with concrete playbooks for structuring AI initiatives that scale across business units, embed governance without slowing innovation, and turn AI from technical capability into strategic value creation.