Summary: AI governance should accelerate value delivery—not slow it down. This session focuses on how CFOs and CIOs can fund AI initiatives with clear ROI, measurable productivity gains, and built-in controls that reduce rework and risk.

Outcome: Real ROI framing, not hype

Takeaway: A practical scorecard for 'AI use cases worth funding'

Key Discussion Points:

• Where AI wins first: forecasting cadence, close acceleration, variance explanations, spend controls
• Defining success: 'fewer manual touches' + faster decisions + higher confidence
• Picking the right use cases: high frequency, high friction, high impact workflows
• Data readiness requirements: what 'decision-grade data' means for AI
• Human-in-the-loop design: where humans approve, override, and validate
• Guardrails without slowing delivery (policies, approvals, model usage boundaries)
• Model risk realities: hallucinations, drift, explainability, auditability
• Usage controls: data access boundaries, prompt governance, logging, retention
• A 90-day AI pipeline that doesn't turn into shelfware (pilot → measure → scale)
• Scaling responsibly: standard patterns, reusable controls, and change management