The AI Governance & Operations Manager is responsible for establishing and operating the AI governance framework across SMBC Asia Pacific, ensuring AI solutions are introduced and managed responsibly, securely, and in accordance with applicable regulatory, risk, data, privacy, security, and technology requirements.

As part of the AI Cross-Functional Team (AI CFT), the role coordinates AI use-case intake, governance reviews, approvals, control monitoring, platform operations, management reporting, and audit readiness. The incumbent will work across Business, Technology, Risk, Compliance, Legal, Data, Cybersecurity, Architecture, and Audit functions to enable responsible innovation while maintaining effective oversight throughout the AI solution lifecycle.

Key Responsibilities

1. AI Governance Framework and Operating Model

• Establish, maintain, and operate the AI governance framework, processes, forums, approval workflows, and reporting mechanisms across APAC.
• Implement AI policies, standards, procedures, control requirements, and governance checkpoints across the end-to-end AI solution lifecycle.
• Maintain clear roles, responsibilities, decision rights, and escalation paths across Business, Technology, Risk, Compliance, Data, Security, Legal, and Audit stakeholders.
• Maintain governance inventories and repositories covering AI use cases, models, agents, copilots, AI-enabled applications, owners, risk classifications, approvals, and lifecycle status.
• Continuously improve governance processes to balance responsible control with delivery speed and business value.

2. AI Use-Case Intake and Lifecycle Governance

• Manage the intake, assessment, triage, prioritization, and governance tracking of AI use cases submitted by business and technology teams.
• Coordinate required reviews and approvals across Architecture, Cybersecurity, Technology Risk, Compliance, Legal, Data Privacy, Data Governance, and other control functions.
• Ensure governance requirements are addressed at key lifecycle stages, including discovery, design, development, testing, deployment, monitoring, material change, and retirement.
• Track use-case ownership, intended outcomes, data usage, risk classification, outstanding actions, approval status, production readiness, and post-implementation obligations.
• Identify governance bottlenecks and facilitate timely resolution or escalation.

3. Responsible AI, Risk and Control Management

• Embed Responsible AI principles covering accountability, fairness, transparency, explainability, privacy, security, human oversight, traceability, and appropriate use.
• Facilitate AI risk and control assessments and ensure required mitigations, approval conditions, and control evidence are documented and tracked.
• Maintain AI-related risk, issue, action, exception, and dependency registers, with clear ownership and target resolution dates.
• Coordinate periodic control reviews and monitor remediation of identified gaps, audit findings, policy exceptions, and technical debt.
• Support incident management and escalation for AI-related security, privacy, compliance, operational, model, data, or conduct concerns.

4. AI Platform Governance and Operational Oversight

• Support governance and operational oversight of approved AI platforms and services, including Microsoft Copilot Studio, Microsoft Foundry, Power Platform, Databricks AI, Azure AI services, and related enterprise tools.
• Coordinate governance requirements for environment strategy, access management, licensing, data loss prevention, connector usage, knowledge-source onboarding, publishing, and production deployment.
• Partner with platform owners to define onboarding, release, monitoring, support, incident, change, and retirement procedures.
• Monitor platform usage, access, capacity, licensing, compliance, operational performance, and control adherence, escalating exceptions where required.
• Promote consistent application of governance standards across business units, delivery teams, and vendors.

5. Governance Forums, Reporting and Audit Readiness

• Coordinate AI governance forums, review boards, steering committees, and decision-making meetings, including agenda preparation, pre-read materials, decisions, actions, and follow-up.
• Develop and maintain governance dashboards and management reporting covering the AI portfolio, risk profile, approvals, exceptions, incidents, control effectiveness, adoption, and value realization.
• Provide concise, decision-oriented updates to senior management and governance stakeholders.
• Maintain complete and traceable governance evidence to support internal reviews, regulatory inquiries, risk assessments, and audits.
• Coordinate responses and remediation activities arising from audit, compliance, risk, or regulatory reviews.

6. Awareness, Training and Change Enablement

• Develop AI governance playbooks, procedures, checklists, templates, guidance, and communication materials.
• Deliver awareness and training to Business, Product, Technology, Data, and Delivery teams on Responsible AI, governance expectations, approval requirements, and permitted use.
• Advise project teams on governance requirements early in the lifecycle to reduce rework and accelerate compliant delivery.
• Promote a culture of responsible experimentation, clear accountability, and sustainable AI adoption across APAC.
• Mentor governance analysts and contribute to the development of AI governance capabilities and communities of practice.

Job Requirements

• Bachelor's Degree in Information Technology, Computer Science, Engineering, Data Science, Business, Risk Management, Law, or a related discipline.
• Minimum 10 years of experience in technology governance, IT risk, operational risk, technology controls, compliance, data governance, project governance, or technology operations, preferably within Banking or Financial Services.
• Proven experience establishing or operating governance frameworks, control processes, risk management programmes, or compliance initiatives in a complex organization.
• Working knowledge of Artificial Intelligence, Generative AI, AI agents, copilots, machine learning, workflow automation, and associated governance considerations.
• Strong understanding of Responsible AI, model and technology risk, cybersecurity, data privacy, data governance, operational resilience, third-party risk, and audit requirements.
• Experience coordinating cross-functional reviews and approvals across Business, Technology, Risk, Compliance, Legal, Security, Data, Architecture, and Audit teams.
• Experience managing governance forums, inventories, risk and issue registers, control evidence, management reporting, and audit or regulatory responses.
• Familiarity with Microsoft Copilot Studio, Microsoft Foundry, Power Platform, Databricks AI, Azure AI services, or comparable enterprise AI platforms will be advantageous.
• Experience in process design, workflow improvement, reporting automation, or governance tooling will be advantageous.
• Strong stakeholder management, facilitation, written communication, and executive presentation skills.
• Ability to translate policy and control requirements into practical, scalable, and clearly understood operating procedures.
• Strong analytical capability, attention to detail, and ability to manage multiple governance activities and priorities.
• Experience within a regulated banking environment and familiarity with APAC regulatory expectations will be highly desirable.
• Relevant certifications in risk, audit, compliance, data privacy, information security, AI governance, or project management will be an advantage.

Key Competencies

• AI governance and Responsible AI — applies clear governance principles and practical controls across the AI lifecycle.
• Risk and control acumen — identifies material risks, defines proportionate controls, and ensures effective remediation and evidence.
• Operational discipline — converts policies and standards into repeatable, measurable, and sustainable governance operations.
• Cross-functional influence — builds alignment across Business, Technology, Risk, Compliance, Legal, Data, Security, Architecture, and Audit.
• Enabling mindset — balances governance requirements with innovation speed, usability, and business value.
• Executive communication — presents governance status, risks, options, implications, and required decisions clearly and concisely.
• Continuous improvement — simplifies governance processes, reduces friction, and identifies opportunities for automation and reuse.
• Accountability and integrity — maintains transparency, traceability, and sound judgement in governance decisions and escalations.