Corporate Planning & Management

Portfolio leadership and opportunity sourcing
Proactively identify high-impact AI/ML opportunities across functions and regions; craft value theses, business cases, and clear success metrics. Assess feasibility, dependencies, and alignment with strategy; quantify benefits (cost, risk reduction, cycle-time, accuracy) and change impact. Map and exploit synergies across use cases (shared data, platforms, models, workflows) to maximize reuse and reduce duplication. Roadmaps, prioritization, and governance Build phased roadmaps with milestones, resource plans, and expected outcomes; apply transparent prioritization criteria: value, risk, readiness, feasibility. Establish program governance, OKRs, stage gates, and benefits realization; run quarterly portfolio reviews with senior leadership. Ensure adherence to model risk management, privacy, security, and responsible AI policies in partnership with Risk, Legal, and Compliance. Execution and change management Convert opportunities into delivery plans; manage timelines, budgets, vendors, and inter-team dependencies. Lead pilots to scaled production with product managers and engineering; embed governance/SDLC, data quality controls, and integration into enterprise workflows. Drive change management, training, and operating-model updates to ensure adoption and sustained value realization. Determine functions that need to be in place to manage, govern and execute AI-native and AI-augmented solutions (e.g., monitoring and testing function) Execute creation of such functions with required budgeting discussions, mobility opportunities with upgraded skills Transparency, communication, and stakeholder influence Create executive dashboards covering value capture, delivery health, risk posture, and adoption; communicate progress, challenges, and decisions crisply. Build a global community of practice across control, finance, risk, and GCCs to promote knowledge-sharing and asset reuse. Influence senior stakeholders and resolve cross-functional trade-offs quickly and constructively. Innovation and thought leadership Scout emerging AI capabilities and patterns relevant to financial services controls, risk, accounting and related operations; assess buy vs build and vendor ecosystems. Define reference architectures, data/product standards, and delivery playbooks to increase velocity and quality. Champion responsible AI, experimentation with guardrails, and continuous improvement.