Leads enterprise AI governance and transformation by operationalizing Responsible AI principles into controls, processes, decision rights, and accountabilities. Integrates governance risks, dependencies, milestones, and actions into transformation PMO activities; coordinates Technology, Security, Privacy, Legal, Risk, and Business Unit stakeholders; facilitates executive governance forums; tracks readiness and adoption; and supports transition to sustainable business-as-usual operating models.
The role translates enterprise AI governance principles, policies and controls into practical ways of working across the Business Units, while ensuring governance requirements, risks, decisions, dependencies and actions are integrated into the overall transformation.
The successful candidate will work across AI, Technology, IT, Security, Privacy, Legal, Risk, Purchasing and business teams to ensure Responsible AI is embedded in how AI use cases are assessed, approved, deployed, adopted and monitored.
This is a hands-on role requiring strong AI governance knowledge, transformation experience, programme discipline and the confidence to influence and challenge senior stakeholders without direct authority. Deep software engineering, R&D or Sales expertise is not required.
Responsible AI & AI Governance
- Act as the principal interface between enterprise Responsible AI and AI Governance teams, the Business Units and the AI Transformation PMO.
- Operationalise the enterprise Responsible AI and AI Governance framework by translating principles and policies into practical controls, processes, decision points and accountabilities.
- Ensure Responsible AI requirements are embedded across the AI lifecycle, including use-case intake, risk classification, assessment, approval, deployment, adoption and ongoing monitoring.
- Work with Business Units to identify and address gaps in AI governance readiness, ownership, controls and assurance, helping teams navigate governance requirements effectively.
- Coordinate across Security, Privacy, Legal, Risk, Technology and other specialist functions so governance requirements operate as an integrated model rather than disconnected controls.
- Maintain an integrated view of AI governance risks, issues, control gaps, exceptions, approvals and decisions, escalating material matters through the appropriate forums.
- Support clear decision rights and accountability between central governance functions, Business Units and enabling teams.
- Promote governance that is proportionate to the risk and nature of each AI use case, balancing responsible adoption, business value and speed of execution.
- Identify recurring governance issues and bottlenecks across Business Units and drive simplification, standardisation and continuous improvement.
- Support the evolution of the AI governance operating model as enterprise AI adoption, technology and regulatory expectations mature.
Business Unit Engagement & Adoption
- Act as a trusted point of connection for Business Units on Responsible AI, AI Governance and transformation execution.
- Translate central governance requirements into clear, practical guidance that Business Units can adopt and apply.
- Ensure Business Unit stakeholders understand what is required, why it is required, who owns each decision and what is needed to progress AI use cases.
- Surface common Business Unit requirements, challenges and dependencies into the central AI transformation and governance teams.
- Track Business Unit readiness and adoption of agreed governance processes, identifying where executive intervention or cross-functional resolution is required.
- Support sustainable ownership of AI governance activities as capabilities transition into ongoing business operations.
AI Transformation PMO & Executive Governance
- Integrate Responsible AI and AI Governance milestones, decisions and dependencies into the overall AI transformation plan.
- Maintain a forward-looking view of milestones, governance decisions, risks, issues, dependencies, actions and outcomes.
- Ensure governance dependencies are visible alongside transformation delivery dependencies and do not sit outside programme management.
- Plan and facilitate relevant SteerCos, governance forums and executive reviews, ensuring they conclude with clear decisions, accountable owners and dates.
- Proactively drive actions across Business Units and central functions through to resolution.
- Challenge senior stakeholders constructively when controls, decisions or actions are not progressing and escalate early with clear implications, options and recommendations.
- Provide senior leadership with concise, evidence-based reporting on AI governance readiness, material risks, Business Unit adoption, unresolved decisions and transformation progress.
- Maintain disciplined RAID, decision, dependency and action management across the AI governance elements of the transformation.
Governance Transition & Operating Model
- Support the transition of Responsible AI and AI Governance capabilities from transformation activity into sustainable enterprise and Business Unit operating models.
- Ensure clear ownership of governance processes, controls, decisions, forums, artefacts and outstanding actions as responsibilities transition.
- Identify ambiguity between central governance ownership and Business Unit accountability and drive it to resolution.
- Support repeatable and scalable governance mechanisms that can accommodate increasing AI adoption.
- Capture implementation lessons and recurring issues and feed them into the continuing development of the governance framework and operating model.
Measurement & Reporting
- Define and track indicators of AI governance maturity, readiness and adoption alongside wider transformation measures.
- Report transparently on governance adoption, unresolved risks and exceptions, decision turnaround, control implementation and Business Unit readiness.
- Identify systemic issues requiring enterprise-level intervention rather than treating them as isolated Business Unit problems.
- Produce decision-oriented reporting that clearly distinguishes what is on track, what is at risk and what decision or intervention is required.
Success criteria
- Responsible AI and AI Governance are embedded in the AI transformation rather than operating as a parallel compliance activity.
- Business Units understand the governance framework, their responsibilities and how to navigate it.
- Accountability between central functions and Business Units is clear for governance decisions, controls and outcomes.
- AI governance is practical, proportionate and scalable, supporting responsible enterprise adoption.
- Governance risks, exceptions, dependencies and decisions are visible through the Transformation PMO and escalated early.
- Executive forums receive a clear view of material Responsible AI and AI Governance issues and make timely decisions.
- Recurring issues across Business Units are identified and addressed systematically.
- Governance capabilities transition into sustainable business-as-usual ownership.
- Senior leadership has confidence that AI adoption is progressing with appropriate governance, accountability and controls.
Essential
- 15+ years of experience across transformation, programme governance, technology governance, risk or complex change in global organisations.
- Demonstrable recent experience in Responsible AI, enterprise AI Governance, AI assurance, data governance or closely related technology governance.
- Experience operationalising governance frameworks by converting principles and policies into practical processes, controls, decision rights and accountabilities.
- Good understanding of the AI lifecycle and the governance considerations associated with assessing, approving, deploying, adopting and monitoring enterprise AI use cases.
- Strong experience working across Business Units and central functions, including Technology, Risk, Security, Privacy, Legal, Compliance or operational teams.
- Strong Transformation PMO and programme governance capability, including SteerCos, RAID and dependency management, decisions, actions, executive reporting and escalation.
- Proven ability to influence and constructively challenge senior stakeholders without direct organisational authority.
- Ability to translate complex governance and technology issues into clear business implications, options and recommendations.
- Experience operating in a large, global and highly matrixed organisation.
- Excellent executive-level written and verbal communication.
Desirable
- Experience designing or implementing an enterprise Responsible AI or AI Governance framework.
- Experience developing AI governance operating models involving both central governance functions and decentralised Business Unit ownership.
- Experience with AI risk assessment, AI use-case governance, AI assurance, model governance or related controls.
- Experience transitioning transformation capabilities into business-as-usual operating models.
- Experience in technology, telecommunications, financial services, consulting or another large global enterprise.
- Familiarity with evolving Responsible AI frameworks and regulatory expectations.
Education
- Bachelor's degree or equivalent professional experience in technology, engineering, business or a related discipline.
- Relevant postgraduate qualifications, such as an MBA, MSc or equivalent, are desirable.
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