Data – AI Agents, Vice President

The Vice President, Data – AI Agents will lead the day-to-day delivery of the Data workstream supporting enterprise AI and agentic AI capabilities.

The role is responsible for ensuring AI-agent use cases have access to authoritative, high-quality, appropriately governed, permissioned and traceable data and that data requirements are understood and addressed before solutions progress into production.

Working closely with Business, Data, AI Engineering, Architecture, Information Security, Identity and Access Management, Risk and Governance teams, the VP will translate AI use-case requirements into practical data solutions and drive them through implementation.

Key Responsibilities Lead Data workstream delivery for assigned AI-agent use cases from initial assessment through production.

Translate business and AI-agent requirements into clear data requirements, dependencies and delivery plans.

Identify required structured and unstructured data sources and establish the appropriate authoritative source.

Coordinate with Data Owners and Data Stewards to confirm ownership, permitted usage and data readiness.

Perform and coordinate AI Data Readiness assessments, identifying gaps and driving remediation.

Assess data quality, freshness, completeness and suitability for AI-agent use.

Support data patterns for APIs, retrieval-augmented generation (RAG), grounding, enterprise search and semantic retrieval.

Ensure appropriate metadata, classification, lineage and provenance are established for information consumed by AI agents.

Work with Security and IAM teams to implement appropriate user, agent and service-identity entitlements.

Ensure AI retrieval and grounding solutions respect underlying data-access restrictions and do not create unintended access paths.

Support requirements for agent memory, contextual information, AI-generated data, retention and traceability.

Define and coordinate data validation and testing before production deployment.

Establish appropriate operational monitoring for data availability, freshness, quality, access and retrieval.

Track data issues, dependencies and risks and escalate material blockers to the VP Data Stream Lead and relevant governance forums.

Identify opportunities to reuse existing enterprise data capabilities rather than creating bespoke integrations for individual agents.

Maintain documentation and evidence required to demonstrate that Data requirements and controls have been addressed.

Core Areas of AccountabilityVP will have practical delivery responsibility across six areas:
1. Data Discovery Determine what information an AI agent requires and where the authoritative information resides.
2. Data Readiness Assess whether data is sufficiently accessible, controlled, current and fit for the intended AI use case.
3. Data Access & Entitlements Work with Security/IAM to ensure agents operate within appropriate user, role and service-identity permissions.
4. Data Quality Identify data-quality limitations that could materially affect agent responses, recommendations or actions.
5. Grounding & Provenance Ensure solutions using approaches such as RAG can trace relevant AI outputs back to appropriate enterprise information sources.
6. Production Controls Ensure data dependencies are monitored after deployment and that failures or deterioration can be identified and escalated.

Key Deliverables The role will typically be responsible for producing and maintaining:

AI Data Readiness assessments

Data source inventories for AI-agent use cases

Source-to-use-case mappings

Data ownership and stewardship documentation

Data classification and entitlement requirements

Data quality assessments and remediation plans

Data lineage and provenance documentationRAG/grounding data requirements

Data integration requirements

Data-related risks, issues and dependencies

Production data monitoring requirements

Data readiness status for governance and implementation forums

Required Qualifications and Experience Strong experience in data management, data architecture, data governance, data analysis or data transformation.

Practical experience delivering complex data initiatives within a large enterprise.

Strong understanding of data quality, metadata, lineage, classification and data lifecycle concepts.

Working understanding of Generative AI and large language model concepts, particularly RAG, grounding and enterprise search.

Understanding of structured and unstructured enterprise information.

Knowledge of data-access controls, entitlements and…