1. Financial Institution’s AI capability

• Clarity of AI strategy, vision and leadership commitment
• Economic value generated from AI across the business
• AI integration into decisions and customer interactions (%)
• Workforce enablement and AI-augmented productivity
• AI risk governance, oversight and responsible AI practices

2. Data and technology foundation for AI

• % of priority data domains integrated and AI-ready
• Centralised model platform (ModelOps/MLOps) maturity
• Number and growth of production-grade AI/ML models
• Cloud, architecture and infrastructure innovations enabling AI
• Core system scalability and performance to support AI workloads

3. Business impact of the AI initiative

• Revenue or measurable value uplift attributable to the initiative
• Cost reduction, productivity gain or operational efficiency improvement
• Customer experience improvement (NPS, satisfaction, service metrics)
• Reduction in process turnaround time or manual effort
• Customer, usage or transaction growth driven by the initiative

4. Accuracy, reliability and risk governance of the AI initiative

• Model performance (accuracy, precision, recall)
• Reduction in False Positives / False Negatives (%)
• Model drift monitoring and corrective action frequency
• Incident rate and controls ensuring fairness, explainability and compliance

5. Scale and adoption of the AI initiative

• Increase in automated or AI-powered decisions/interactions (%)
• User adoption (customers, employees or ecosystem partners)
• Workforce adoption and change management effectiveness
• Expansion across business units, products or markets