Lead and govern the bank-wide portfolio of Artificial Intelligence programs, ensuring alignment with strategic objectives, regulatory expectations, and enterprise risk frameworks. The role is accountable for end-to-end program delivery across AI initiatives, including prioritization, execution oversight, value realization, and governance, while ensuring responsible AI adoption at scale.
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Key Result Areas
- AI Program Governance & Delivery
Establish and manage centralized governance for all AI programs. Ensure structured lifecycle management from ideation to deployment and scaling.
- Portfolio Prioritization & Resource Optimization
Drive prioritization aligned with business value, regulatory requirements, and resource constraints. Optimize utilization of AI infrastructure (e.g. GPU, platforms) and delivery capacity.
- Execution Oversight
Track progress, risks, dependencies, and ensure timely delivery of AI initiatives. Implement standardized program management frameworks and reporting.
- Value Realization
Define and monitor KPIs linked to business outcomes (revenue, cost, risk reduction, efficiency). Ensure measurable and auditable value realization.
- AI Governance & Compliance
Ensure all programs comply with internal governance, Model Risk, and regulatory (e.g. CBUAE) guidelines. Embed responsible AI principles (fairness, explainability, security, auditability).
- Stakeholder Management
Engage with senior stakeholders across business, risk, compliance, and technology. Provide executive-level reporting (SteerCo, GCEO updates).
- Vendor & Partner Management
Oversee delivery partners, vendors, and strategic collaborations. Ensure alignment with contractual, delivery, and quality expectations.
Decision Making Authority & Responsibility
- Accountable for AI program portfolio governance and delivery outcomes.
- Recommends prioritization, funding allocation, and execution strategies.
- Escalates critical risks and trade-offs to senior leadership as required.
- Approves program-level frameworks, reporting standards, and governance controls.
Knowledge, Skills and Experience
- Education: Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred.
- Experience: 12–15+ years of experience in technology program management, with strong exposure to AI/ML initiatives. Proven track record managing large-scale, cross-functional transformation programs. Experience in banking or financial services preferred.
- Skills: Strong program management and portfolio governance expertise. Understanding of AI/ML lifecycle, data platforms, and enterprise architecture. Knowledge of regulatory and risk frameworks applicable to AI. Stakeholder management at an executive level. Strong analytical, communication, and decision-making skills.
