AI projects continue to multiply, but proving their value remains difficult. Most organisations can track activity, not impact. Dashboards count pilots and models, yet few translate to measurable business outcomes. The result is familiar: success stories without clarity on what they actually delivered.
The real issue is measurement. Traditional ROI metrics fail when AI changes decisions more than results. Financial indicators miss the operational gains, while qualitative benefits often sound too vague to defend. Without clear evidence, budgets come under scrutiny and confidence erodes.
This session focuses on how to connect AI work with business outcomes through structured metrics, governance, and accountability. We’ll explore how value tracking evolves from experimentation to scale, which indicators earn trust at board level, and where measurement stops being meaningful. A closed exchange for comparing methods, tools, and lessons learned in defining, proving, and sustaining AI impact.
