Senior Manager, AI Governance & Portfolio Management

Location: New York City, Midtown Manhattan – Fashion District (On-Site)

Department: IT

Reports To: SVP, Technology & Digital Innovation

Position Summary: The Senior Manager, AI Governance & Portfolio Management will be responsible for operationalizing G-III’s enterprise AI strategy by establishing and executing the governance, operating model, measurement, and oversight frameworks required to scale AI responsibly across the organization. This role goes beyond defining governance policies—it ensures governance is embedded into how AI is executed across the organization through clear ownership, operating cadence, measurable KPIs, risk management, and continuous improvement. Working closely with the SVP, Technology & Digital Innovation, the AI Team, Data, IT, Legal, Security, Privacy, business leaders, and external transformation partners, this role will act as the control tower for AI initiatives, ensuring that AI adoption is scalable, consistent, measurable, and aligned with business value while managing risk, quality, and performance.

Key Responsibilities: AI Governance & Operating ModelDefine and operationalize a layered governance model across data governance (quality, ownership, certification), AI governance (model behavior, monitoring, standards), and experience governance (usability, trust, adoption).Establish governance structures that connect enterprise AI strategy and standards with business execution. Maintain a central governance backlog of initiatives, actions, issues, risks, and priorities.

Governance Execution & Cadence: Implement governance as an ongoing operating rhythm, including monthly governance reviews, quarterly maturity assessments, and continuous backlog tracking and prioritization. Coordinate and support the AI CoE Steering Committee, including agenda development, initiative reviews, decision tracking, and follow-up actions. Ensure governance is executed through measurable actions and accountability, not just policy definition.

Intake, Prioritization & Use Case Oversight: Design and manage structured intake processes for AI use cases across brands, functions, and divisions. Establish prioritization frameworks based on business impact, strategic alignment, feasibility, risk, compliance, and required investment. Maintain the enterprise AI use-case inventory, including status, owner, business value, risk, investment, adoption, and next decision point.

Pilot Framework & Measurement: Define standardized frameworks for AI pilots, including baseline measurement, success criteria, KPI design, and graduation thresholds. Ensure pilots are evaluated across operational efficiency, quality and accuracy, business impact, scalability, and risk. Track outcomes and identify initiatives that should scale, be refined, or stop.

Governance KPIs & Maturity Tracking: Define and monitor governance KPIs across AI adoption visibility, data quality and consistency, model accuracy and reliability, explainability and transparency, compliance adherence, and issue resolution. Establish and maintain governance maturity assessments aligned with the NIST AI Risk Management Framework, ISO/IEC AI governance standards, and enterprise data governance best practices. Provide regular visibility into governance performance, maturity, risks, and improvement priorities.

Ownership Model & Cross-Functional Alignment: Define and coordinate governance responsibilities across Data teams (data quality, ownership, certification), IT teams (infrastructure, security, compliance monitoring), and the AI function (guidelines, development standards, and alignment with Legal).Partner with Legal, Security, Privacy, HR, business teams, and external partners to ensure clear accountability across the AI lifecycle. Drive cross-functional issue resolution and escalate decisions when appropriate.

Vendor & Tool Governance: Support the evaluation and governance of AI platforms, vendors, and tools for enterprise fit, business value, risk, security, and scalability. Define consistent frameworks for build-versus-buy decisions and technology selection. Help prevent tool fragmentation by ensuring consistent evaluation, approval, and adoption standards across the organization.

Enterprise AI Landscape & Reusability: Maintain visibility of the enterprise AI landscape across products, use cases, models, agents, platforms, services, and vendors. Identify opportunities to reuse existing AI capabilities before approving development of new solutions. Advocate principles for reusable AI components, common services, reference architectures, and approved technology patterns. Partner with Data Architecture and IT Architecture to prevent duplication, unnecessary platform proliferation, and inconsistent solution patterns. Risk Management & Responsible AIIdentify, track, and help mitigate risks including unreliable AI outputs, loss of trust or low adoption, data/privacy concerns, compliance and regulatory issues, operational rework, and lack of scalability. Ensure AI initiatives meet enterprise standards for accuracy, transparency, reliability, responsible use, and appropriate human oversight.

Experience Governance & Adoption Quality: Define standards that help ensure AI outputs are understandable, trusted, and usable by business users. Partner with the AI Business Analyst to improve adoption, usability, output quality, and user experience while maintaining clear separation between governance oversight and business-facing execution.

Post-Launch Monitoring & Value Realization: Monitor AI initiatives after implementation to confirm expected adoption, business value, quality, reliability, and risk outcomes continue to be achieved. Identify performance gaps and coordinate corrective actions, optimization, or reevaluation when expected outcomes are not realized. Provide leadership with clear visibility into realized value versus original business cases and success criteria.

Continuous Improvement: Establish feedback loops from pilot performance, production performance, user adoption, incidents, and error patterns. Continuously refine governance frameworks, operating processes, standards, and controls based on results, business feedback, and the evolving AI landscape.

Who You Are: Structured Thinking: Breaks down complex problems into clear frameworks and repeatable processes. Operational Rigor: Ensures disciplined execution, accountability, and consistency across governance and workflows. Governance Mindset: Balances innovation and speed with appropriate risk, compliance, and control. Analytical Problem Solving: Uses data and structured reasoning to evaluate performance, risk, and business outcomes. Cross-Functional Collaboration: Aligns stakeholders across business, technology, data, legal, risk, and external partners. Executive Communication: Synthesizes governance performance, risks, and recommendations into concise, decision-ready insights. Influence Without Authority: Drives alignment and decisions across teams through clear communication, credibility, and reasoning. Adaptability: Operates effectively in an evolving environment where frameworks and priorities continue to mature.5–8+ years of experience in program management, PMO, operations, governance, risk, or transformation roles. Demonstrated experience operationalizing governance or enterprise operating models through recurring reviews, KPI tracking, issue/risk management, and cross-functional accountability. Experience designing, implementing, or supporting governance frameworks, operating models, or enterprise control processes. Demonstrated experience in product ownership or portfolio ownership for AI, data, analytics, or digital products, including defining business outcomes, prioritizing capabilities, managing product lifecycle decisions, and measuring value realization. Strong analytical and structured problem-solving skills, including the ability to define measurable outcomes and translate data into recommendations. Experience working cross-functionally with business, technology, risk, legal, security, or data teams. Strong written and verbal communication skills with the ability to translate governance, risk, and technology concepts into clear business language. Ability to manage ambiguity, prioritize competing demands, and operate effectively in a rapidly evolving environment. Hands-on exposure to AI, data, analytics, or digital transformation initiatives preferred. Familiarity with enterprise AI platforms and tools such as ChatGPT Enterprise, Microsoft 365 Copilot, Azure AI services, or similar ecosystems preferred. Understanding of responsible AI principles, data governance, enterprise risk management, and frameworks such as NIST AI RMF or ISO/IEC 42001 preferred. Experience supporting vendor evaluation, technology selection, or build-versus-buy decisions preferred. Experience with enterprise architecture for AI, data science, or data platforms, including evaluation of platform capabilities, reusable AI services, integration patterns, build-versus-buy decisions, and alignment between AI solutions and the broader enterprise technology landscape preferred. Experience in retail, apparel, ecommerce, consumer goods, or a cross-brand/multi-region organization is a plus.