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 Accountability
VP 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 documentation
RAG/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 identity/access-management principles.
Experience working with business and technology stakeholders to define requirements and deliver data solutions.
Strong problem-solving skills with the ability to identify data dependencies and drive issues to resolution.
Strong written and verbal communication skills, including the ability to explain complex data issues to non-technical stakeholders.
Experience in a regulated or highly controlled environment is preferred.
Preferred Experience
Experience supporting AI, machine learning or advanced analytics solutions.
Familiarity with cloud and enterprise data platforms.
Exposure to technologies such as Microsoft Azure AI Foundry, Microsoft Purview, Collibra, Databricks or Snowflake.
Understanding of APIs, data lakes/lakehouses, semantic or vector search and enterprise knowledge-management solutions.
Experience working with Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC).