SVP, Head of AI

Enterprise AI Strategy & Portfolio

Define, drive, and oversee the bank's enterprise AI strategy and roadmap, aligned to business priorities, measurable value, risk appetite, and the bank's broader technology and data strategy.

Lead the AI Use Case Committee and own the intake, prioritization, business-case discipline, budget management, and funding recommendations for new AI use cases.

Organize the enterprise AI portfolio around high-value recurring decisions and the five AI value factories: Revenue, Credit, Risk & Compliance, Operations, and Enterprise Efficiency.

Establish consistent value-realization measures for AI initiatives, including financial impact, decision impact, adoption, feasibility, data readiness, risk, and strategic differentiation.

Report AI adoption, cost, delivery progress, risk posture, and realized value to the CIO, Executive Leadership Group, and Board.

AI Delivery & Engineering

Build and lead the AI Engineering Bench, hiring, coaching, and developing AI engineering talent and deploying that capability flexibly across priority business-unit use cases.

Establish a delivery model in which central AI provides enterprise standards, reusable capabilities, technical expertise, and implementation support, while business units retain ownership of their processes, adoption, outcomes, and benefits realization.

Lead business processes and decision redesign around AI, taking priority use cases from discovery and workflow mapping through pilot, production, adoption, monitoring, and measurable outcomes.

Own build-versus-buy-versus-configure recommendations, favoring configuration of mature vendor platforms where appropriate and proprietary development where the bank has a differentiated data or workflow advantage.

Partner with Data Engineering and business data owners to establish controlled data paths for priority AI use cases and leverage the bank's Data Foundation as it matures.

Create reusable agents, skills, prompts, patterns, and implementation assets that can be safely shared across use cases and business units.

Governance, Risk & Controls

Partner with Model Risk Management, Risk, Compliance, Information Security, ITGC, Legal, and Third-Party Risk Management to keep the bank's AI governance framework current as adoption scales.

Operationalize responsible-AI principles including safety and soundness, ethics and fairness, transparency and explainability, human-in-the-loop review, compliance by design, security and privacy, and third-party risk management.

Operate the end-to-end AI governance lifecycle, including use-case registration, risk tiering, testing and validation, approval, human oversight, production monitoring, incident and fallback procedures, control evidence, and retirement.

Own enterprise AI vendor and platform strategy, including platform selection, contract renewals, cost management, concentration risk, and ongoing capability assessment.

Adoption, Education & Workforce

Drive bank-wide AI education, adoption, and usage monitoring through role-based training, AI champions, demonstrations, and practical use-case enablement.

Partner with HR and business leaders to incorporate AI fluency into hiring and leadership expectations, identify roles and workflows that should be redesigned, and redirect productivity gains toward higher-value work.

Build a culture in which AI is used as a governed operating capability rather than an isolated technology experiment.

Involvement with interviewing and hiring decisions.

Prepare and deliver employee performance evaluations, goal planning, and counseling.

Manage, support, coach and train employees.