Global Head of AI, MD

What matters most

• Set a business-wide AI roadmap

  Define and execute AI priorities aligned with State Street’s enterprise objectives, selecting opportunities for measurable efficiency, experience, and revenue outcomes.

• Govern the AI portfolio

  Maintain visibility across use cases, pilots, and implementations; lead executive steering forums; and establish KPIs for adoption, scalability, business impact, and value realization.

• Turn ideas into adopted solutions

  Oversee employee AI idea programs and work with R&D, data science, and engineering teams to translate business priorities into development direction, monitor delivery, and assess adoption.

• Connect business and enterprise leadership

  Serve as the liaison to enterprise AI leadership, aligning business initiatives with enterprise strategy, standards, governance, and investment priorities while elevating business needs and outcomes.

• Manage external AI partnerships

  Evaluate vendors and partnerships for business value and fit with security, regulatory, and architecture expectations; coordinate oversight with procurement, legal, risk, and technology teams.

Likely challenges to think through

• Prioritizing competing AI opportunities

  Likely preparation challenge: efficiency, experience, and growth opportunities may compete for investment. Prepare a decision framework that weighs expected value, feasibility, risk, and scalability without assuming any particular initiative is already approved.

• Moving beyond pilot metrics

  Likely preparation challenge: a delivered pilot may not translate into sustained adoption or realized value. Prepare to distinguish delivery milestones from adoption and business-outcome measures, including how results would inform scale-or-stop decisions.

• Balancing experimentation with controls

  Likely preparation challenge: encouraging employee ideas and evaluating external tools must coexist with enterprise standards and vendor security and regulatory expectations. Prepare an approach for testing ideas while involving the appropriate governance and risk partners before broader deployment.

Topics to prepare

• Enterprise AI operating models

  Review how business leaders, enterprise AI leadership, R&D, data science, and engineering can divide decisions and delivery responsibilities. Exercise: sketch decision rights for a hypothetical AI initiative from proposal through rollout. Relevance: the posting requires deep understanding of AI/ML and enterprise AI operating models, plus alignment across these groups.

• AI strategy and investment tradeoffs

  Exercise: compare three hypothetical opportunities for cost reduction, client experience, and revenue growth; explain how you would rank them against enterprise priorities. Relevance: the role defines the business-wide AI roadmap and advises senior leaders on technologies, risks, and strategic opportunities.

• Cross-business governance

  Exercise: outline which decisions a governance forum should make, what evidence it needs, and when an issue should go to an executive steering committee. Relevance: the role coordinates initiatives across business lines and leads both forums and steering committees.

• Value realization and adoption

  Exercise: distinguish pilot success from scaled business impact using hypothetical measures for efficiency, adoption, client or employee experience, and revenue. Relevance: the posting calls for KPIs covering initiative success, value realization, adoption, and scalability.

• Responsible innovation and external risk

  Exercise: identify the questions you would raise before advancing an employee-submitted idea or external AI solution, including security, regulatory expectations, and enterprise architecture. Relevance: these considerations appear in the posting; experience in regulated financial services or a similarly complex enterprise is preferred.

Skills to revise

• Draft a prioritized AI roadmap

  Practice writing a one-page roadmap for hypothetical use cases, showing business outcomes, sequencing, and alignment with enterprise priorities. This rehearses the stated responsibility to define and execute the business-wide AI vision and roadmap.

• Present a portfolio decision

  Prepare a short mock steering-committee briefing that distinguishes use cases, pilots, and implementations and recommends which to advance, pause, or reassess. This reflects the required portfolio view, executive influence, and decision-making discipline.

• Design a KPI framework

  For one hypothetical initiative, specify a baseline, adoption measure, business-outcome measure, and test for scalability; explain how each would be reviewed. The role explicitly establishes KPIs and tracks efficiency gains and business impact.

• Triage employee AI ideas

  Create a simple evaluation rubric and apply it to two hypothetical employee submissions, stating what evidence would justify a pilot. This practices the posted responsibility to evaluate, prioritize, and advance employee-driven opportunities.

• Translate priorities for delivery teams

  Turn a hypothetical business objective into a concise brief for R&D, data science, and engineering, including the intended outcome and progress questions. This reflects the role’s delivery oversight and responsibility to give actionable development direction.

• Evaluate a vendor proposal

  Draft questions for a hypothetical AI provider on business value, security, regulatory expectations, architecture fit, contract oversight, and performance. This practices the stated vendor-management responsibility alongside procurement, legal, risk, and technology teams.

• Select an enterprise transformation walkthrough

  Prepare one real example, if available, showing your leadership role, how an initiative scaled across teams, the decisions you influenced, and measured business outcomes. The posting requires 12+ years of relevant leadership and a track record of scaling enterprise initiatives.

• Draft a business-wide AI roadmap

  As a preparation exercise, outline how you would prioritize opportunities in cost, productivity, client and employee experience, and revenue. Show how the roadmap would align with enterprise priorities; label any assumptions about State Street.

• Build a sample portfolio and KPI view

  Create a one-page example covering use cases, pilots, and implementations, with proposed measures for adoption, scalability, business impact, and value realization. Be ready to explain how you would use it to make investment decisions.

• Rehearse an executive governance example

  Prepare a real example, if available, of leading a cross-functional forum or steering committee. Explain how you secured decisions, resolved competing priorities, and kept business and technology leaders accountable.

• Sketch an employee-idea-to-delivery process

  As a design exercise, show how submitted AI ideas could be evaluated, prioritized, and passed to R&D, data science, or engineering, with checkpoints for responsible innovation, delivery progress, and adoption.

• Prepare a vendor partnership case

  Use a real example, if available, to explain solution evaluation, stakeholder involvement, contract or performance oversight, and realized value. Be ready to discuss security, regulatory expectations, and enterprise architecture, which the posting names as considerations.

• Write questions for the employer

  Ask how this business coordinates with State Street’s enterprise AI leadership, what decisions its AI steering committees own, and which measures of adoption and business value matter most for this role.