Data & AI Portfolio, Adoption & Service Excellence

About the Role

The Data & AI Lead is responsible for identifying, shaping, and delivering AI-enabled products that improve risk and compliance processes and deliver measurable business value. The role sits within the AI Centre of Excellence (CoE) and acts as the bridge between business, data, AI engineering, and governance teams.

Key Responsibilities

• Strategy:
• Define and maintain the Data & AI capability strategy and product roadmap for Risk & Compliance.
• Own the AI use case portfolio across assisted, enabled, generative, and agentic automation.
• Partner closely with respective Product Owners, Business Owners, and Process Owners to identify where AI, data, automation, and analytics can improve productivity, risk outcomes, user experience, and decision quality.
• Lead structured opportunity discovery to identify areas where process engineering through AI and data tools can deliver substantial benefits.
• Challenge existing ways of working by bringing practical ideas on GenAI, agentic AI, workflow automation, data intelligence, and reusable AI capability patterns.
• Support strategic buy, build, reuse, or partner decisions for AI capabilities.
• Maintain a centralized AI backlog and product pipeline.
• Business:
• Act as the primary business-facing lead for AI capability development.
• Build trusted relationships with Product Owners, Business Owners, SMEs, and senior stakeholders.
• Translate business challenges into clear, outcome-led AI opportunities.
• Lead process and value analysis to determine where AI and data tools can generate measurable business impact.
• Quantify expected benefits before implementation and track realized benefits after deployment.
• Enable product and business owners to reimagine work using AI, including GenAI assistants, copilots, agentic workflows, intelligent triage, automated summarization, insight generation, and decision-support tools.
• Ensure AI products are business-relevant, user-centered, measurable, scalable, and designed for sustained adoption.
• Processes:
• Establish and operate a structured AI product lifecycle.
• Facilitate end-to-end product journey and process engineering reviews to identify improvements through AI and data-enabled solutions.
• Convert business and process opportunities into well-defined product artifacts.
• Work closely with AI engineering, data and analytics teams, GenAI prompt specialists, governance teams, and business SMEs.
• Oversee use case delivery from ideation through deployment.
• Define and maintain value measurement approaches.
• Use feedback to continuously improve AI products, backlog priorities, and product journey design.
• People & Talent:
• Build and own strong working relationships with Product Owners, Business Owners, Process Owners, Technology, Data, Risk, Compliance, and AI Governance teams.
• Act as a trusted advisor by helping teams understand possibilities with AI, GenAI, agentic AI, data products, and automation.
• Facilitate ideation sessions, product clinics, journey walkthroughs, and design workshops.
• Build confidence and adoption by partnering with learning, engagement, and change teams.
• Promote a product and value-led mindset across AI delivery teams.
• Encourage a culture of curiosity, experimentation, and disciplined execution.
• Support the development of AI fluency across business stakeholders.
• Risk Management:
• Ensure AI use cases are shaped and delivered in line with enterprise AI governance, responsible AI, data, risk, compliance, security, and regulatory expectations.
• Work with Product Owners and Business Owners to identify risks across the full product journey.
• Partner with AI Governance, Risk, Compliance, and assurance teams to ensure appropriate validation, explainability, bias assessment, control requirements, monitoring inputs, and approval evidence are built into product delivery.
• Ensure AI-enabled process engineering does not compromise control effectiveness, accountability, data integrity, customer outcomes, regulatory expectations, or operational resilience.
• Identify and manage delivery, dependency, value realization, user adoption, and operational readiness risks.
• Ensure AI products are implemented with appropriate human oversight, usage boundaries, business readiness, control documentation, monitoring inputs, and feedback mechanisms.
• Use performance insights to identify where deployed AI products may require refinement.
• Governance:
• Maintain transparent governance over the AI product portfolio.
• Ensure each AI use case has clear ownership.
• Provide regular reporting to senior stakeholders and governance forums.
• Support governance forums with evidence-based recommendations.
• Maintain disciplined portfolio controls so AI initiatives are traceable, measurable, outcome-led, and aligned to business priorities.
• Ensure product journey reviews, benefit cases, design decisions, user impacts, adoption evidence, and value realization outcomes are documented.
• Create a continuous improvement governance loop.

Key Deliverables

• AI product roadmap & portfolio backlog (including generative & agentic AI initiatives) aligned to Risk & Compliance strategic priorities.
• End-to-end process re-engineering blueprint co-created with product/business owners (leveraging generative & agentic AI) to transform key Risk & Compliance processes.
• Central AI use case intake & prioritization framework.
• AI adoption strategy & integration plan (with clear adoption KPIs).
• Business enablement playbook & training modules.
• AI value measurement framework & dashboards tracking qualitative and quantitative benefits.
• AI governance compliance documentation and requisite risk & compliance approvals.
• Executive portfolio performance reports.

Requirements & Tech Stack

• Experience in product management, data analytics, or digital transformation.
• Strong knowledge of risk, compliance, or financial services operations.
• Ability to operate at the intersection of business, data, and technology.
• Strong stakeholder engagement and strategic thinking capabilities.

Key Stakeholders

• AI Engineering: Solution development
• Data & Analytics: Data products and datasets
• GenAI Specialists: Prompt design and context engineering
• AI Assurance: Model validation and governance
• AI Operations: Monitoring and lifecycle management
• Business Units: AI adoption and value realization