AI Governance Manager

The Role

Teneo is seeking an experienced AI Governance Manager to help design, implement, and operationalise the firm’s approach to responsible AI governance and enablement.

This role sits at the intersection of AI enablement, information security, data governance, privacy, legal, compliance, risk, and technology. You will help ensure AI use cases across the firm are assessed, governed, documented, monitored, and enabled in a consistent, risk-based, and business-aligned way.

The role is weighted toward governance, risk, and control, while also supporting responsible AI adoption through practical guidance, stakeholder education, and clear enablement pathways. You will help teams understand how to use AI safely and effectively, while ensuring the right guardrails, evidence, approvals, and oversight are in place.

This role is suited to someone who understands the opportunities and risks associated with AI adoption, particularly generative AI, agentic AI, SaaS AI features, and third-party AI platforms. You do not need to be a hands-on machine learning engineer, but you should be comfortable engaging with technical, risk, and business stakeholders to evaluate AI use cases, identify risks, define controls, and support responsible adoption.

You will play a key role in helping Teneo enable innovation while maintaining strong governance, auditability, security, privacy, and client trust.

Responsibilities

AI Governance, Risk & Controls - Primary Focus

AI Governance Framework & Controls

• Support the design, implementation, and continuous improvement of Teneo’s AI governance framework, including policies, standards, procedures, controls, and operating processes.
• Help define and embed responsible AI principles across the organisation, including transparency, accountability, fairness, privacy, security, human oversight, and appropriate use.
• Develop practical governance processes that guide AI initiatives from idea to approval, implementation, monitoring, and retirement in a controlled and repeatable way.
• Align AI governance practices with broader information security, privacy, legal, data governance, third-party risk, and enterprise risk frameworks.
• Support the development of clear, auditable documentation and control evidence for AI-related decisions, approvals, risks, exceptions, and mitigations.

AI Use Case Lifecycle Management

• Partner with AI enablement, technology, data, legal, privacy, security, and business stakeholders to guide AI use cases through appropriate review and governance pathways.
• Support intake, triage, assessment, approval, and ongoing review of AI use cases, including generative AI tools, internal AI solutions, third-party AI platforms, and embedded AI capabilities in SaaS products.
• Ensure AI use cases are evaluated for business value, duplication, data sensitivity, security risk, privacy considerations, model/output risk, operational resilience, and alignment with enterprise standards.
• Maintain AI inventories, use case registers, attestations, and supporting documentation for approved and production AI solutions.
• Help define requirements for production readiness, including ownership, monitoring, access controls, data handling, testing, human oversight, and exit or retirement considerations.

AI Risk Assessment & Responsible AI Assurance

• Conduct or support AI risk assessments to identify risks related to data exposure, privacy, bias, explainability, output reliability, intellectual property, security, vendor dependency, and misuse.
• Work with technical and business stakeholders to define appropriate controls and mitigations based on the risk profile of each AI use case.
• Support the review of third-party AI tools and platforms, working closely with Third Party Risk, Legal, Privacy, Procurement, and Information Security teams.
• Ensure AI governance activities produce consistent, traceable, and defensible artefacts to support internal audit, client assurance, regulatory reviews, and management oversight.
• Monitor emerging AI-related regulatory, industry, and supervisory developments and help assess their impact on Teneo’s governance approach.

Metrics, Reporting & Executive Communication

• Define and maintain AI governance metrics, dashboards, benchmarks, and key risk indicators to provide visibility into AI adoption, risk posture, governance activity, and control effectiveness.
• Prepare materials for leadership, governance forums, committees, and other stakeholders, including dashboards, summaries, issue papers, and presentations.
• Support regular reporting on AI initiatives, approved use cases, emerging risks, exceptions, remediation actions, and governance maturity.
• Translate complex AI governance and risk topics into clear, business-focused language for senior stakeholders and non-technical audiences.

AI Enablement & Adoption - Supporting Focus

Stakeholder Partnership & Responsible AI Enablement

• Act as a trusted advisor to business, technology, data, legal, compliance, privacy, security, and risk stakeholders on responsible AI use and governance expectations.
• Help teams navigate AI governance processes efficiently, ensuring they understand what is required, why it matters, and how to progress AI use cases responsibly.
• Support education, awareness, and enablement activities that help employees understand how to use AI tools safely, securely, and in line with Teneo’s policies.
• Partner with AI enablement and technology teams to identify common friction points in the governance process and improve guidance, templates, workflows, and user experience.
• Promote a pragmatic governance culture that enables responsible innovation rather than creating unnecessary friction.
• Support the development of reusable guidance, playbooks, checklists, and FAQs to help business teams assess and progress AI opportunities consistently.

Requirements

Must-Have

• 5 - 8+ years of experience in technology risk, cybersecurity governance, data governance, privacy, compliance, digital risk, consulting, AI governance, or related fields.
• Strong understanding of AI governance, responsible AI, data risk, technology risk, and control frameworks.
• Experience supporting or managing cross-functional governance processes involving technology, legal, privacy, security, compliance, risk, and business stakeholders.
• Practical understanding of generative AI, machine learning, agentic AI, SaaS AI features, or AI-enabled business tools and the risks they introduce.
• Ability to evaluate AI use cases from a risk and governance perspective, including data usage, security, privacy, explainability, human oversight, and operational impact.
• Strong written and verbal communication skills, with the ability to translate technical and regulatory concepts into practical business guidance.
• Strong stakeholder management skills and the ability to influence teams without direct authority.
• Comfortable operating in an evolving environment where frameworks, processes, and ways of working are still being defined.

Desirable

• Experience building or maturing AI governance, responsible AI, model risk, data governance, technology risk, or digital transformation governance programmes.
• Exposure to AI governance frameworks, standards, or regulations such as NIST AI RMF, ISO/IEC 42001, ISO 27001, GDPR, EU AI Act concepts, or similar.
• Experience supporting AI inventories, use case registers, governance committees, control testing, audit readiness, or regulatory/client assurance activities.
• Experience assessing third-party AI vendors, SaaS platforms, LLM-enabled tools, or AI-enabled workflow automation.
• Familiarity with MLOps, LLMOps, model monitoring, AI lifecycle controls, prompt/output testing, or production readiness concepts.
• Experience developing guidance, playbooks, training, or enablement materials for non-technical stakeholders.
• Relevant certifications such as CISM, CISSP, CRISC, ISO 42001, privacy certifications, data governance certifications, or AI governance cr