AI Project Portfolio Lead

About the Role:
The AI Project Portfolio Lead provides structured oversight of the company's enterprise AI portfolio while ensuring that business opportunities are translated into practical, scalable and well-governed AI solutions. The role maintains a consolidated view of use cases, pilots and projects – ensuring priorities, milestones, risks, dependencies and decisions remain visible and actively managed – and works with AI Champions, business teams and technology stakeholders to identify and qualify use cases, define requirements and business value, shape solution designs and coordinate delivery from concept through prototype, pilot and production release. Working closely with project owners, sponsors and enabling functions, the role strengthens delivery discipline, reinforces accountability and drives timely resolution of risks and blockers. Acting as a portfolio-focused coordination role, it aligns solutioning and technical delivery in partnership with the Forward Deployed AI Lead and Data Scientists and provides the Head of AI and senior governance forums with clear, reliable and decision-oriented reporting on portfolio performance, delivery confidence, value realization and areas requiring intervention.

Key Accountabilities

• Own the day-to-day oversight of the enterprise AI portfolio across AI Champions, Business Transformation and strategic AI initiatives, maintaining a consolidated view of use cases, pilots and projects with current ownership, scope, status, milestones and next steps.
• Maintain the enterprise AI pipeline and roadmap, and support prioritization of initiatives based on strategic alignment, business value, delivery feasibility, risk, capacity and dependencies.
• Work with AI Champions, business teams and senior stakeholders to identify, assess and qualify AI opportunities aligned with strategic objectives and measurable business needs.
• Translate business problems into structured requirements, user journeys and BRDs, and define business cases, success KPIs, service expectations, adoption measures and benefit-realization plans.
• Shape practical AI solution designs using copilots, agents, machine learning, analytics and workflow automation, and coordinate high-level solution and integration requirements with Architecture, Technology and platform teams on environments, APIs, security and deployment.
• Assess data readiness by identifying required data sources, owners, quality issues, access needs and gaps, and coordinate ingestion, masking, anonymization and environment preparation.
• Establish and manage regular portfolio review and reporting cadences, track projects against agreed plans, milestones, deliverables and KPIs, and structure delivery into prototype, pilot and scale phases with clear release criteria.
• Identify project-level and portfolio-level risks, issues and dependencies across Business, Technology, Data, Architecture, Information Security, Risk, Legal, Compliance and Procurement, and follow mitigation actions through to resolution.
• Reinforce accountability by ensuring project owners maintain clear actions, decisions, commitments and target dates, and prepare use cases for the Data Architecture Review Board (DARB), Information Security, Risk and other governance reviews through to completion.
• Guide AI Champions and business teams through use-case scoping, prototyping, testing and user acceptance testing (UAT) while maintaining consistent delivery standards.
• Coordinate the Forward Deployed AI Lead and Data Scientists to ensure solutions are technically sound, secure, measurable and aligned with transformation goals.
• Consolidate use-case needs into required AI Factory capabilities (data, tools, environments, models, integrations and guardrails) and coordinate production-readiness activities, including sandbox integration, security and performance testing, UAT, documentation, handover and release planning.
• Establish consistent reporting standards, status definitions, delivery-confidence assessments, governance routines and escalation mechanisms, and maintain dashboards covering status, milestones, risks, dependencies, resource needs, business value and benefit realization.
• Prepare decision-oriented portfolio updates for the Head of AI, Transformation, Governance and Steering Committees, and drive continuous improvement across portfolio and solutioning methods, standards, reusable components and ways of working.

Experience, Skills & Success Profile

Experience & Qualifications

• Significant experience across portfolio, program and project management and AI solutioning, business analysis, solution architecture, product management or technology delivery.
• Experience overseeing a portfolio of projects, use cases or transformation initiatives across multiple business and technology teams, and translating complex business problems into technology, AI, analytics or automation solutions.
• Experience within financial services, fintech, payments or another regulated and data-intensive environment is preferred.
• Bachelor’s degree in business, technology, engineering, computer science, information systems, data science or finance, or equivalent professional experience; a relevant project, program or portfolio certification (PMP, PRINCE2, MSP, PgMP, PfMP or equivalent) is advantageous.
• Strong understanding of portfolio planning, project governance, reporting, prioritization, risk and dependency management, delivery assurance, and of data requirements, integration patterns, APIs, cloud environments, security controls and enterprise architecture.
• Strong understanding of AI solution patterns (generative AI, copilots, agents, machine learning, analytics and workflow automation) and experience preparing business requirements, user journeys, solution designs, business cases, delivery plans and success measures.
• Experience coordinating solutions through architecture, information security, risk, data and governance approval processes, and leading initiatives from discovery and prototype through pilot, testing, production deployment and operational handover.
• Experience working with multidisciplinary teams, including business stakeholders, engineers, data scientists, architects, security teams and project managers, and preparing executive-level updates, governance materials and decision papers.
• Familiarity with enterprise AI platforms, low-code tools, cloud AI services, and project and portfolio management and data visualization tools, with the ability to operate with pace, accuracy and sound judgment in a fast-moving, commercially focused environment.

Skills & Competencies

• Demonstrates strong portfolio, program and project management discipline alongside solid business analysis and solution-design capability.
• Translates ambiguous business challenges into clear requirements, practical solution options and measurable outcomes, and converts complex project and solution information into concise, decision-oriented reporting.
• Understands the strengths, limitations, risks and applications of different AI technologies, balancing strategic ambition with technical feasibility, data readiness, business value, risk and delivery practicality.
• Builds trusted relationships and manages stakeholders with confidence, credibility and professionalism, adapting messages for project teams, business leaders, technical stakeholders and senior governance forums.
• Facilitates discovery workshops, challenges assumptions and helps stakeholders define the underlying problem, using data, dashboards, business cases and KPIs to support prioritization and investment decisions.
• Maintains strong attention to detail across requirements, documentation, testing and governance, identifying risks and dependencies early and following through persistently until resolved.
• Plans and prioritizes effectively across multiple projects, use cases, delivery phases and stakeholders, distinguishing project-level issues from systemic portfolio-level constraints, and collaborates across Business, AI, Technology, Architecture, Data, Information Security and Risk without relying solely on formal authority.
• Provides clear direction and coaching to the Forward Deployed AI Lead, Data Scientists, AI Champions and business project teams, and promotes reusable solution patterns, common standards and scalable capabilities.
• Maintains high standards of governance, transparency and documentation while avoiding unnecessary bureaucracy, and adapts quickly to evolving AI technologies, platforms and delivery practices.

What Success Looks Like in This Role

• The organization has a complete, accurate and current view of its enterprise AI portfolio, and high-value opportunities are identified, qualified and prioritized against clear business and strategic criteria.
• Business problems are translated into clear requirements, measurable outcomes and practical solution designs, with credible business cases, success KPIs, delivery plans and benefit-realization measures.
• Solutions are designed using appropriate AI patterns and align with enterprise architecture, data, security and governance standards, with data, integration and environment requirements identified early enough to prevent avoidable delays.
• AI projects and use cases are reviewed through a consistent governance cadence, moving efficiently through DARB, Information Security, Risk and other approvals, with risks, issues, dependencies and bottlenecks identified early, assigned clear owners and resolved or escalated promptly.
• Delivery is structured across prototype, pilot and scale phases with clear stage criteria; production releases are secure, tested, documented and supported by effective operational handover; and reusable capabilities and AI Factory requirements are developed.
• Leadership receives clear, decision-oriented reporting on portfolio performance, delivery confidence and value realization, while the Forward Deployed AI Lead, Data Scientists, AI Champions and business teams receive clear priorities and technical direction.
• Stakeholders view the role as trusted, commercially aware, technically credible and responsive, and the AI portfolio demonstrates stronger predictability, transparency, accountability and measurable business impact.