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The AI Portfolio Lead is a senior individual contributor responsible for driving operational excellence, portfolio governance, and execution cadence across a divisional AI portfolio. The role owns the management rhythms that keep AI initiatives aligned, transparent, and moving from demand intake through adoption and value realization, including portfolio dashboards, governance forums, decision logs, action tracking, duplicate-project reviews, risk and dependency tracking, and leadership-ready reporting. This role requires strong program management, analytical, stakeholder management, and communication skills to provide leaders with clear visibility into portfolio health, priorities, decisions, outcomes, and risks while partnering across business, product, technology, finance, change, delivery, and governance teams.

• Portfolio Governance and Operating Cadence: Establish and maintain the divisional AI portfolio operating cadence, including intake reviews, governance forums, leadership reviews, planning cycles, and performance review rhythms.
• Portfolio Governance and Operating Cadence: Manage the divisional AI backlog, including demand intake, prioritization support, cohort planning, readiness tracking, and alignment to business priorities.
• Portfolio Governance and Operating Cadence: Own risk, issue, action, decision, and dependency tracking, including decision logs, action registers, milestone visibility, and follow-through routines so commitments and escalations are visible and addressed.
• Portfolio Governance and Operating Cadence: Lead duplicate-project reviews and portfolio rationalization efforts to identify overlap, improve resource utilization, and align AI investments to the highest-value opportunities.
• Executive Reporting, Analytics, and Portfolio Transparency: Develop and maintain executive dashboards, KPIs, scorecards, and portfolio analytics that provide clear visibility into initiative health, performance, risks, decisions, and outcomes.
• Executive Reporting, Analytics, and Portfolio Transparency: Prepare leadership-ready portfolio materials that synthesize complex information into concise insights, decision points, and recommended actions.
• Executive Reporting, Analytics, and Portfolio Transparency: Drive reporting consistency, data quality, and governance standards across the divisional AI portfolio.
• Executive Reporting, Analytics, and Portfolio Transparency: Maintain project hygiene and portfolio transparency across intake, prioritization, delivery, adoption, and value-realization stages.
• Change, Adoption, and Enablement: Own business-specific adoption planning, including impacted roles, stakeholder engagement, enablement needs, communications, and reinforcement approach.
• Change, Adoption, and Enablement: Coordinate training reinforcement, leadership talking points, adoption communications, and feedback loops after launch.
• Change, Adoption, and Enablement: Identify adoption risks, resistance patterns, readiness gaps, and operational blockers; recommend practical actions to improve uptake and sustainability.
• Change, Adoption, and Enablement: Facilitate alignment across business, product, technology, finance, delivery, and operational teams to resolve conflicts and optimize portfolio outcomes.
• Finance, Value, and Benefits Realization: Partner with business finance and controllership stakeholders to validate baselines, benefit formulas, assumptions, and realized impact.
• Finance, Value, and Benefits Realization: Support KPI design, including direct and indirect benefit definitions, operational metrics, expected value, and value tracking cadence.
• Finance, Value, and Benefits Realization: Track expected versus realized value and surface variance drivers when benefits are unclear, delayed, or at risk.
• Finance, Value, and Benefits Realization: Connect portfolio prioritization and rationalization decisions to business value, resource utilization, and measurable outcomes.
• Governance, Risk, and Continuous Improvement: Ensure AI Hub intake submissions are complete, clear, and ready for governance review.
• Governance, Risk, and Continuous Improvement: Support risk-tier pre-checks, documentation quality, human oversight planning, controls alignment, and reassessment trigger tracking.
• Governance, Risk, and Continuous Improvement: Help business stakeholders understand responsible AI governance requirements and complete them efficiently without slowing responsible innovation.
• Governance, Risk, and Continuous Improvement: Continuously improve portfolio management processes, tools, reporting frameworks, and governance practices.

Bachelor's degree in Business, Information Systems, Engineering, Project Management, or a related field, or equivalent practical e