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).

Experience supporting data governance, data readiness or technology-risk assessments.

Leadership Capabilities

The successful candidate will demonstrate:

Delivery leadership: Drives multiple data activities and dependencies through to completion.

Technical credibility: Understands data and AI concepts sufficiently to challenge proposed solutions and identify risks.

Problem solving: Works through complex data ownership, quality, access and integration issues.

Stakeholder management: Builds effective relationships across Business, Data, Technology and control functions.

Risk awareness: Recognizes when a data issue could materially affect AI-agent outputs or actions.

Constructive challenge: Questions the authority, quality and accessibility of data rather than assuming technical availability means suitability.

Execution discipline: Maintains clear ownership, actions, dependencies, risks and evidence across assigned use cases.

Key Stakeholders

The role will work closely with:

• VP / Data Stream Lead
• AI Product and Agent teams
• Business Use-Case Owners
• Data Owners and Data Stewards

AI Engineering

• Data Engineering
• Enterprise and Solution Architecture
• Information Security
• Identity and Access Management
• Risk, Compliance and Privacy

AI Governance

Production Operations

Measures of Success

Success will be demonstrated through:

Timely completion of Data Readiness assessments.

Identification of authoritative sources and accountable data owners.

Resolution or appropriate escalation of material data-quality and access issues.

Appropriate entitlement controls for AI-agent data access.

Traceability and provenance of data used by AI solutions.

Reduction of avoidable bespoke data integrations through reuse of enterprise capabilities.

Effective identification and management of production data issues.

Clear and timely reporting of Data risks, dependencies and readiness.

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